diff --git a/.pylintrc b/.pylintrc
index eb913c202..d95e1a8b1 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -99,7 +99,7 @@ evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / stateme
 [BASIC]
 
 # Good variable names which should always be accepted, separated by a comma
-good-names=i,j,k,ex,Run,_,d,e,v,o,l,x,ts
+good-names=i,j,k,ex,Run,_,d,e,v,o,l,x,ts,f
 
 # Bad variable names which should always be refused, separated by a comma
 bad-names=foo,bar,baz,toto,tutu,tata,d,fd
diff --git a/superset/cli.py b/superset/cli.py
index a694db4b1..e8af9ccc7 100755
--- a/superset/cli.py
+++ b/superset/cli.py
@@ -159,7 +159,7 @@ def load_examples_run(load_test_data):
     data.load_country_map_data()
 
     print('Loading [Multiformat time series]')
-    data.load_multiformat_time_series_data()
+    data.load_multiformat_time_series()
 
     print('Loading [Paris GeoJson]')
     data.load_paris_iris_geojson()
diff --git a/superset/data/__init__.py b/superset/data/__init__.py
index 49873eb23..96f16e391 100644
--- a/superset/data/__init__.py
+++ b/superset/data/__init__.py
@@ -1,2518 +1,16 @@
-"""Loads datasets, dashboards and slices in a new superset instance"""
-# pylint: disable=C,R,W
-import datetime
-import gzip
-import json
-import os
-import random
-import textwrap
-
-import geohash
-import pandas as pd
-import polyline
-from sqlalchemy import BigInteger, Date, DateTime, Float, String, Text
-
-from superset import app, db
-from superset.connectors.connector_registry import ConnectorRegistry
-from superset.connectors.sqla.models import TableColumn
-from superset.models import core as models
-from superset.utils.core import get_or_create_main_db, readfile
-
-# Shortcuts
-DB = models.Database
-Slice = models.Slice
-Dash = models.Dashboard
-
-TBL = ConnectorRegistry.sources['table']
-
-config = app.config
-
-DATA_FOLDER = os.path.join(config.get('BASE_DIR'), 'data')
-
-misc_dash_slices = set()  # slices assembled in a "Misc Chart" dashboard
-
-
-def update_slice_ids(layout_dict, slices):
-    charts = [
-        component for component in layout_dict.values()
-        if isinstance(component, dict) and component['type'] == 'CHART'
-    ]
-    sorted_charts = sorted(charts, key=lambda k: k['meta']['chartId'])
-    for i, chart_component in enumerate(sorted_charts):
-        if i < len(slices):
-            chart_component['meta']['chartId'] = int(slices[i].id)
-
-
-def merge_slice(slc):
-    o = db.session.query(Slice).filter_by(slice_name=slc.slice_name).first()
-    if o:
-        db.session.delete(o)
-    db.session.add(slc)
-    db.session.commit()
-
-
-def get_slice_json(defaults, **kwargs):
-    d = defaults.copy()
-    d.update(kwargs)
-    return json.dumps(d, indent=4, sort_keys=True)
-
-
-def load_energy():
-    """Loads an energy related dataset to use with sankey and graphs"""
-    tbl_name = 'energy_usage'
-    with gzip.open(os.path.join(DATA_FOLDER, 'energy.json.gz')) as f:
-        pdf = pd.read_json(f)
-    pdf.to_sql(
-        tbl_name,
-        db.engine,
-        if_exists='replace',
-        chunksize=500,
-        dtype={
-            'source': String(255),
-            'target': String(255),
-            'value': Float(),
-        },
-        index=False)
-
-    print("Creating table [wb_health_population] reference")
-    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
-    if not tbl:
-        tbl = TBL(table_name=tbl_name)
-    tbl.description = "Energy consumption"
-    tbl.database = get_or_create_main_db()
-    db.session.merge(tbl)
-    db.session.commit()
-    tbl.fetch_metadata()
-
-    slc = Slice(
-        slice_name="Energy Sankey",
-        viz_type='sankey',
-        datasource_type='table',
-        datasource_id=tbl.id,
-        params=textwrap.dedent("""\
-        {
-            "collapsed_fieldsets": "",
-            "groupby": [
-                "source",
-                "target"
-            ],
-            "having": "",
-            "metric": "sum__value",
-            "row_limit": "5000",
-            "slice_name": "Energy Sankey",
-            "viz_type": "sankey",
-            "where": ""
-        }
-        """),
-    )
-    misc_dash_slices.add(slc.slice_name)
-    merge_slice(slc)
-
-    slc = Slice(
-        slice_name="Energy Force Layout",
-        viz_type='directed_force',
-        datasource_type='table',
-        datasource_id=tbl.id,
-        params=textwrap.dedent("""\
-        {
-            "charge": "-500",
-            "collapsed_fieldsets": "",
-            "groupby": [
-                "source",
-                "target"
-            ],
-            "having": "",
-            "link_length": "200",
-            "metric": "sum__value",
-            "row_limit": "5000",
-            "slice_name": "Force",
-            "viz_type": "directed_force",
-            "where": ""
-        }
-        """),
-    )
-    misc_dash_slices.add(slc.slice_name)
-    merge_slice(slc)
-
-    slc = Slice(
-        slice_name="Heatmap",
-        viz_type='heatmap',
-        datasource_type='table',
-        datasource_id=tbl.id,
-        params=textwrap.dedent("""\
-        {
-            "all_columns_x": "source",
-            "all_columns_y": "target",
-            "canvas_image_rendering": "pixelated",
-            "collapsed_fieldsets": "",
-            "having": "",
-            "linear_color_scheme": "blue_white_yellow",
-            "metric": "sum__value",
-            "normalize_across": "heatmap",
-            "slice_name": "Heatmap",
-            "viz_type": "heatmap",
-            "where": "",
-            "xscale_interval": "1",
-            "yscale_interval": "1"
-        }
-        """),
-    )
-    misc_dash_slices.add(slc.slice_name)
-    merge_slice(slc)
-
-
-def load_world_bank_health_n_pop():
-    """Loads the world bank health dataset, slices and a dashboard"""
-    tbl_name = 'wb_health_population'
-    with gzip.open(os.path.join(DATA_FOLDER, 'countries.json.gz')) as f:
-        pdf = pd.read_json(f)
-    pdf.columns = [col.replace('.', '_') for col in pdf.columns]
-    pdf.year = pd.to_datetime(pdf.year)
-    pdf.to_sql(
-        tbl_name,
-        db.engine,
-        if_exists='replace',
-        chunksize=50,
-        dtype={
-            'year': DateTime(),
-            'country_code': String(3),
-            'country_name': String(255),
-            'region': String(255),
-        },
-        index=False)
-
-    print("Creating table [wb_health_population] reference")
-    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
-    if not tbl:
-        tbl = TBL(table_name=tbl_name)
-    tbl.description = readfile(os.path.join(DATA_FOLDER, 'countries.md'))
-    tbl.main_dttm_col = 'year'
-    tbl.database = get_or_create_main_db()
-    tbl.filter_select_enabled = True
-    db.session.merge(tbl)
-    db.session.commit()
-    tbl.fetch_metadata()
-
-    defaults = {
-        "compare_lag": "10",
-        "compare_suffix": "o10Y",
-        "limit": "25",
-        "granularity_sqla": "year",
-        "groupby": [],
-        "metric": 'sum__SP_POP_TOTL',
-        "metrics": ["sum__SP_POP_TOTL"],
-        "row_limit": config.get("ROW_LIMIT"),
-        "since": "2014-01-01",
-        "until": "2014-01-02",
-        "time_range": "2014-01-01 : 2014-01-02",
-        "where": "",
-        "markup_type": "markdown",
-        "country_fieldtype": "cca3",
-        "secondary_metric": "sum__SP_POP_TOTL",
-        "entity": "country_code",
-        "show_bubbles": True,
-    }
-
-    print("Creating slices")
-    slices = [
-        Slice(
-            slice_name="Region Filter",
-            viz_type='filter_box',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type='filter_box',
-                date_filter=False,
-                groupby=['region', 'country_name'])),
-        Slice(
-            slice_name="World's Population",
-            viz_type='big_number',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                since='2000',
-                viz_type='big_number',
-                compare_lag="10",
-                metric='sum__SP_POP_TOTL',
-                compare_suffix="over 10Y")),
-        Slice(
-            slice_name="Most Populated Countries",
-            viz_type='table',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type='table',
-                metrics=["sum__SP_POP_TOTL"],
-                groupby=['country_name'])),
-        Slice(
-            slice_name="Growth Rate",
-            viz_type='line',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type='line',
-                since="1960-01-01",
-                metrics=["sum__SP_POP_TOTL"],
-                num_period_compare="10",
-                groupby=['country_name'])),
-        Slice(
-            slice_name="% Rural",
-            viz_type='world_map',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type='world_map',
-                metric="sum__SP_RUR_TOTL_ZS",
-                num_period_compare="10")),
-        Slice(
-            slice_name="Life Expectancy VS Rural %",
-            viz_type='bubble',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type='bubble',
-                since="2011-01-01",
-                until="2011-01-02",
-                series="region",
-                limit=0,
-                entity="country_name",
-                x="sum__SP_RUR_TOTL_ZS",
-                y="sum__SP_DYN_LE00_IN",
-                size="sum__SP_POP_TOTL",
-                max_bubble_size="50",
-                filters=[{
-                    "col": "country_code",
-                    "val": [
-                        "TCA", "MNP", "DMA", "MHL", "MCO", "SXM", "CYM",
-                        "TUV", "IMY", "KNA", "ASM", "ADO", "AMA", "PLW",
-                    ],
-                    "op": "not in"}],
-            )),
-        Slice(
-            slice_name="Rural Breakdown",
-            viz_type='sunburst',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type='sunburst',
-                groupby=["region", "country_name"],
-                secondary_metric="sum__SP_RUR_TOTL",
-                since="2011-01-01",
-                until="2011-01-01",)),
-        Slice(
-            slice_name="World's Pop Growth",
-            viz_type='area',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                since="1960-01-01",
-                until="now",
-                viz_type='area',
-                groupby=["region"],)),
-        Slice(
-            slice_name="Box plot",
-            viz_type='box_plot',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                since="1960-01-01",
-                until="now",
-                whisker_options="Min/max (no outliers)",
-                viz_type='box_plot',
-                groupby=["region"],)),
-        Slice(
-            slice_name="Treemap",
-            viz_type='treemap',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                since="1960-01-01",
-                until="now",
-                viz_type='treemap',
-                metrics=["sum__SP_POP_TOTL"],
-                groupby=["region", "country_code"],)),
-        Slice(
-            slice_name="Parallel Coordinates",
-            viz_type='para',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                since="2011-01-01",
-                until="2011-01-01",
-                viz_type='para',
-                limit=100,
-                metrics=[
-                    "sum__SP_POP_TOTL",
-                    'sum__SP_RUR_TOTL_ZS',
-                    'sum__SH_DYN_AIDS'],
-                secondary_metric='sum__SP_POP_TOTL',
-                series="country_name",)),
-    ]
-    misc_dash_slices.add(slices[-1].slice_name)
-    for slc in slices:
-        merge_slice(slc)
-
-    print("Creating a World's Health Bank dashboard")
-    dash_name = "World's Bank Data"
-    slug = "world_health"
-    dash = db.session.query(Dash).filter_by(slug=slug).first()
-
-    if not dash:
-        dash = Dash()
-    js = textwrap.dedent("""\
-{
-    "CHART-36bfc934": {
-        "children": [],
-        "id": "CHART-36bfc934",
-        "meta": {
-            "chartId": 40,
-            "height": 25,
-            "sliceName": "Region Filter",
-            "width": 2
-        },
-        "type": "CHART"
-    },
-    "CHART-37982887": {
-        "children": [],
-        "id": "CHART-37982887",
-        "meta": {
-            "chartId": 41,
-            "height": 25,
-            "sliceName": "World's Population",
-            "width": 2
-        },
-        "type": "CHART"
-    },
-    "CHART-17e0f8d8": {
-        "children": [],
-        "id": "CHART-17e0f8d8",
-        "meta": {
-            "chartId": 42,
-            "height": 92,
-            "sliceName": "Most Populated Countries",
-            "width": 3
-        },
-        "type": "CHART"
-    },
-    "CHART-2ee52f30": {
-        "children": [],
-        "id": "CHART-2ee52f30",
-        "meta": {
-            "chartId": 43,
-            "height": 38,
-            "sliceName": "Growth Rate",
-            "width": 6
-        },
-        "type": "CHART"
-    },
-    "CHART-2d5b6871": {
-        "children": [],
-        "id": "CHART-2d5b6871",
-        "meta": {
-            "chartId": 44,
-            "height": 52,
-            "sliceName": "% Rural",
-            "width": 7
-        },
-        "type": "CHART"
-    },
-    "CHART-0fd0d252": {
-        "children": [],
-        "id": "CHART-0fd0d252",
-        "meta": {
-            "chartId": 45,
-            "height": 50,
-            "sliceName": "Life Expectancy VS Rural %",
-            "width": 8
-        },
-        "type": "CHART"
-    },
-    "CHART-97f4cb48": {
-        "children": [],
-        "id": "CHART-97f4cb48",
-        "meta": {
-            "chartId": 46,
-            "height": 38,
-            "sliceName": "Rural Breakdown",
-            "width": 3
-        },
-        "type": "CHART"
-    },
-    "CHART-b5e05d6f": {
-        "children": [],
-        "id": "CHART-b5e05d6f",
-        "meta": {
-            "chartId": 47,
-            "height": 50,
-            "sliceName": "World's Pop Growth",
-            "width": 4
-        },
-        "type": "CHART"
-    },
-    "CHART-e76e9f5f": {
-        "children": [],
-        "id": "CHART-e76e9f5f",
-        "meta": {
-            "chartId": 48,
-            "height": 50,
-            "sliceName": "Box plot",
-            "width": 4
-        },
-        "type": "CHART"
-    },
-    "CHART-a4808bba": {
-        "children": [],
-        "id": "CHART-a4808bba",
-        "meta": {
-            "chartId": 49,
-            "height": 50,
-            "sliceName": "Treemap",
-            "width": 8
-        },
-        "type": "CHART"
-    },
-    "COLUMN-071bbbad": {
-        "children": [
-            "ROW-1e064e3c",
-            "ROW-afdefba9"
-        ],
-        "id": "COLUMN-071bbbad",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT",
-            "width": 9
-        },
-        "type": "COLUMN"
-    },
-    "COLUMN-fe3914b8": {
-        "children": [
-            "CHART-36bfc934",
-            "CHART-37982887"
-        ],
-        "id": "COLUMN-fe3914b8",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT",
-            "width": 2
-        },
-        "type": "COLUMN"
-    },
-    "GRID_ID": {
-        "children": [
-            "ROW-46632bc2",
-            "ROW-3fa26c5d",
-            "ROW-812b3f13"
-        ],
-        "id": "GRID_ID",
-        "type": "GRID"
-    },
-    "HEADER_ID": {
-        "id": "HEADER_ID",
-        "meta": {
-            "text": "World's Bank Data"
-        },
-        "type": "HEADER"
-    },
-    "ROOT_ID": {
-        "children": [
-            "GRID_ID"
-        ],
-        "id": "ROOT_ID",
-        "type": "ROOT"
-    },
-    "ROW-1e064e3c": {
-        "children": [
-            "COLUMN-fe3914b8",
-            "CHART-2d5b6871"
-        ],
-        "id": "ROW-1e064e3c",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW"
-    },
-    "ROW-3fa26c5d": {
-        "children": [
-            "CHART-b5e05d6f",
-            "CHART-0fd0d252"
-        ],
-        "id": "ROW-3fa26c5d",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW"
-    },
-    "ROW-46632bc2": {
-        "children": [
-            "COLUMN-071bbbad",
-            "CHART-17e0f8d8"
-        ],
-        "id": "ROW-46632bc2",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW"
-    },
-    "ROW-812b3f13": {
-        "children": [
-            "CHART-a4808bba",
-            "CHART-e76e9f5f"
-        ],
-        "id": "ROW-812b3f13",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW"
-    },
-    "ROW-afdefba9": {
-        "children": [
-            "CHART-2ee52f30",
-            "CHART-97f4cb48"
-        ],
-        "id": "ROW-afdefba9",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW"
-    },
-    "DASHBOARD_VERSION_KEY": "v2"
-}
-    """)
-    l = json.loads(js)
-    update_slice_ids(l, slices)
-
-    dash.dashboard_title = dash_name
-    dash.position_json = json.dumps(l, indent=4)
-    dash.slug = slug
-
-    dash.slices = slices[:-1]
-    db.session.merge(dash)
-    db.session.commit()
-
-
-def load_css_templates():
-    """Loads 2 css templates to demonstrate the feature"""
-    print('Creating default CSS templates')
-    CSS = models.CssTemplate  # noqa
-
-    obj = db.session.query(CSS).filter_by(template_name='Flat').first()
-    if not obj:
-        obj = CSS(template_name="Flat")
-    css = textwrap.dedent("""\
-    .gridster div.widget {
-        transition: background-color 0.5s ease;
-        background-color: #FAFAFA;
-        border: 1px solid #CCC;
-        box-shadow: none;
-        border-radius: 0px;
-    }
-    .gridster div.widget:hover {
-        border: 1px solid #000;
-        background-color: #EAEAEA;
-    }
-    .navbar {
-        transition: opacity 0.5s ease;
-        opacity: 0.05;
-    }
-    .navbar:hover {
-        opacity: 1;
-    }
-    .chart-header .header{
-        font-weight: normal;
-        font-size: 12px;
-    }
-    /*
-    var bnbColors = [
-        //rausch    hackb      kazan      babu      lima        beach     tirol
-        '#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
-        '#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
-        '#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
-     ];
-    */
-    """)
-    obj.css = css
-    db.session.merge(obj)
-    db.session.commit()
-
-    obj = (
-        db.session.query(CSS).filter_by(template_name='Courier Black').first())
-    if not obj:
-        obj = CSS(template_name="Courier Black")
-    css = textwrap.dedent("""\
-    .gridster div.widget {
-        transition: background-color 0.5s ease;
-        background-color: #EEE;
-        border: 2px solid #444;
-        border-radius: 15px;
-        box-shadow: none;
-    }
-    h2 {
-        color: white;
-        font-size: 52px;
-    }
-    .navbar {
-        box-shadow: none;
-    }
-    .gridster div.widget:hover {
-        border: 2px solid #000;
-        background-color: #EAEAEA;
-    }
-    .navbar {
-        transition: opacity 0.5s ease;
-        opacity: 0.05;
-    }
-    .navbar:hover {
-        opacity: 1;
-    }
-    .chart-header .header{
-        font-weight: normal;
-        font-size: 12px;
-    }
-    .nvd3 text {
-        font-size: 12px;
-        font-family: inherit;
-    }
-    body{
-        background: #000;
-        font-family: Courier, Monaco, monospace;;
-    }
-    /*
-    var bnbColors = [
-        //rausch    hackb      kazan      babu      lima        beach     tirol
-        '#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
-        '#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
-        '#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
-     ];
-    */
-    """)
-    obj.css = css
-    db.session.merge(obj)
-    db.session.commit()
-
-
-def load_birth_names():
-    """Loading birth name dataset from a zip file in the repo"""
-    with gzip.open(os.path.join(DATA_FOLDER, 'birth_names.json.gz')) as f:
-        pdf = pd.read_json(f)
-    pdf.ds = pd.to_datetime(pdf.ds, unit='ms')
-    pdf.to_sql(
-        'birth_names',
-        db.engine,
-        if_exists='replace',
-        chunksize=500,
-        dtype={
-            'ds': DateTime,
-            'gender': String(16),
-            'state': String(10),
-            'name': String(255),
-        },
-        index=False)
-    l = []
-    print("Done loading table!")
-    print("-" * 80)
-
-    print("Creating table [birth_names] reference")
-    obj = db.session.query(TBL).filter_by(table_name='birth_names').first()
-    if not obj:
-        obj = TBL(table_name='birth_names')
-    obj.main_dttm_col = 'ds'
-    obj.database = get_or_create_main_db()
-    obj.filter_select_enabled = True
-
-    if not any(col.column_name == 'num_california' for col in obj.columns):
-        obj.columns.append(TableColumn(
-            column_name='num_california',
-            expression="CASE WHEN state = 'CA' THEN num ELSE 0 END"
-        ))
-
-    db.session.merge(obj)
-    db.session.commit()
-    obj.fetch_metadata()
-    tbl = obj
-
-    defaults = {
-        "compare_lag": "10",
-        "compare_suffix": "o10Y",
-        "limit": "25",
-        "granularity_sqla": "ds",
-        "groupby": [],
-        "metric": 'sum__num',
-        "metrics": ["sum__num"],
-        "row_limit": config.get("ROW_LIMIT"),
-        "since": "100 years ago",
-        "until": "now",
-        "viz_type": "table",
-        "where": "",
-        "markup_type": "markdown",
-    }
-
-    print("Creating some slices")
-    slices = [
-        Slice(
-            slice_name="Girls",
-            viz_type='table',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                groupby=['name'],
-                filters=[{
-                    'col': 'gender',
-                    'op': 'in',
-                    'val': ['girl'],
-                }],
-                row_limit=50,
-                timeseries_limit_metric='sum__num')),
-        Slice(
-            slice_name="Boys",
-            viz_type='table',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                groupby=['name'],
-                filters=[{
-                    'col': 'gender',
-                    'op': 'in',
-                    'val': ['boy'],
-                }],
-                row_limit=50)),
-        Slice(
-            slice_name="Participants",
-            viz_type='big_number',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="big_number", granularity_sqla="ds",
-                compare_lag="5", compare_suffix="over 5Y")),
-        Slice(
-            slice_name="Genders",
-            viz_type='pie',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="pie", groupby=['gender'])),
-        Slice(
-            slice_name="Genders by State",
-            viz_type='dist_bar',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                filters=[{
-                    'col': 'state',
-                    'op': 'not in',
-                    'val': ['other'],
-                }],
-                viz_type="dist_bar",
-                metrics=['sum__sum_girls', 'sum__sum_boys'],
-                groupby=['state'])),
-        Slice(
-            slice_name="Trends",
-            viz_type='line',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="line", groupby=['name'],
-                granularity_sqla='ds', rich_tooltip=True, show_legend=True)),
-        Slice(
-            slice_name="Average and Sum Trends",
-            viz_type='dual_line',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="dual_line", metric='avg__num', metric_2='sum__num',
-                granularity_sqla='ds')),
-        Slice(
-            slice_name="Title",
-            viz_type='markup',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="markup", markup_type="html",
-                code="""\
-    <div style="text-align:center">
-        <h1>Birth Names Dashboard</h1>
-        <p>
-            The source dataset came from
-            <a href="https://github.com/hadley/babynames" target="_blank">[here]</a>
-        </p>
-        <img src="/static/assets/images/babytux.jpg">
-    </div>
-    """)),
-        Slice(
-            slice_name="Name Cloud",
-            viz_type='word_cloud',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="word_cloud", size_from="10",
-                series='name', size_to="70", rotation="square",
-                limit='100')),
-        Slice(
-            slice_name="Pivot Table",
-            viz_type='pivot_table',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="pivot_table", metrics=['sum__num'],
-                groupby=['name'], columns=['state'])),
-        Slice(
-            slice_name="Number of Girls",
-            viz_type='big_number_total',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="big_number_total", granularity_sqla="ds",
-                filters=[{
-                    'col': 'gender',
-                    'op': 'in',
-                    'val': ['girl'],
-                }],
-                subheader='total female participants')),
-        Slice(
-            slice_name="Number of California Births",
-            viz_type='big_number_total',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                metric={
-                    "expressionType": "SIMPLE",
-                    "column": {
-                        "column_name": "num_california",
-                        "expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END",
-                    },
-                    "aggregate": "SUM",
-                    "label": "SUM(num_california)",
-                },
-                viz_type="big_number_total",
-                granularity_sqla="ds")),
-        Slice(
-            slice_name='Top 10 California Names Timeseries',
-            viz_type='line',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                metrics=[{
-                    'expressionType': 'SIMPLE',
-                    'column': {
-                        'column_name': 'num_california',
-                        'expression': "CASE WHEN state = 'CA' THEN num ELSE 0 END",
-                    },
-                    'aggregate': 'SUM',
-                    'label': 'SUM(num_california)',
-                }],
-                viz_type='line',
-                granularity_sqla='ds',
-                groupby=['name'],
-                timeseries_limit_metric={
-                    'expressionType': 'SIMPLE',
-                    'column': {
-                        'column_name': 'num_california',
-                        'expression': "CASE WHEN state = 'CA' THEN num ELSE 0 END",
-                    },
-                    'aggregate': 'SUM',
-                    'label': 'SUM(num_california)',
-                },
-                limit='10')),
-        Slice(
-            slice_name="Names Sorted by Num in California",
-            viz_type='table',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                groupby=['name'],
-                row_limit=50,
-                timeseries_limit_metric={
-                    'expressionType': 'SIMPLE',
-                    'column': {
-                        'column_name': 'num_california',
-                        'expression': "CASE WHEN state = 'CA' THEN num ELSE 0 END",
-                    },
-                    'aggregate': 'SUM',
-                    'label': 'SUM(num_california)',
-                })),
-        Slice(
-            slice_name="Num Births Trend",
-            viz_type='line',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(
-                defaults,
-                viz_type="line")),
-    ]
-    for slc in slices:
-        merge_slice(slc)
-
-    print("Creating a dashboard")
-    dash = db.session.query(Dash).filter_by(dashboard_title="Births").first()
-
-    if not dash:
-        dash = Dash()
-    js = textwrap.dedent("""\
-{
-    "CHART-0dd270f0": {
-        "meta": {
-            "chartId": 51,
-            "width": 2,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-0dd270f0",
-        "children": []
-    },
-    "CHART-a3c21bcc": {
-        "meta": {
-            "chartId": 52,
-            "width": 2,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-a3c21bcc",
-        "children": []
-    },
-    "CHART-976960a5": {
-        "meta": {
-            "chartId": 53,
-            "width": 2,
-            "height": 25
-        },
-        "type": "CHART",
-        "id": "CHART-976960a5",
-        "children": []
-    },
-    "CHART-58575537": {
-        "meta": {
-            "chartId": 54,
-            "width": 2,
-            "height": 25
-        },
-        "type": "CHART",
-        "id": "CHART-58575537",
-        "children": []
-    },
-    "CHART-e9cd8f0b": {
-        "meta": {
-            "chartId": 55,
-            "width": 8,
-            "height": 38
-        },
-        "type": "CHART",
-        "id": "CHART-e9cd8f0b",
-        "children": []
-    },
-    "CHART-e440d205": {
-        "meta": {
-            "chartId": 56,
-            "width": 8,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-e440d205",
-        "children": []
-    },
-    "CHART-59444e0b": {
-        "meta": {
-            "chartId": 57,
-            "width": 3,
-            "height": 38
-        },
-        "type": "CHART",
-        "id": "CHART-59444e0b",
-        "children": []
-    },
-    "CHART-e2cb4997": {
-        "meta": {
-            "chartId": 59,
-            "width": 4,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-e2cb4997",
-        "children": []
-    },
-    "CHART-e8774b49": {
-        "meta": {
-            "chartId": 60,
-            "width": 12,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-e8774b49",
-        "children": []
-    },
-    "CHART-985bfd1e": {
-        "meta": {
-            "chartId": 61,
-            "width": 4,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-985bfd1e",
-        "children": []
-    },
-    "CHART-17f13246": {
-        "meta": {
-            "chartId": 62,
-            "width": 4,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-17f13246",
-        "children": []
-    },
-    "CHART-729324f6": {
-        "meta": {
-            "chartId": 63,
-            "width": 4,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-729324f6",
-        "children": []
-    },
-    "COLUMN-25a865d6": {
-        "meta": {
-            "width": 4,
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "COLUMN",
-        "id": "COLUMN-25a865d6",
-        "children": [
-            "ROW-cc97c6ac",
-            "CHART-e2cb4997"
-        ]
-    },
-    "COLUMN-4557b6ba": {
-        "meta": {
-            "width": 8,
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "COLUMN",
-        "id": "COLUMN-4557b6ba",
-        "children": [
-            "ROW-d2e78e59",
-            "CHART-e9cd8f0b"
-        ]
-    },
-    "GRID_ID": {
-        "type": "GRID",
-        "id": "GRID_ID",
-        "children": [
-            "ROW-8515ace3",
-            "ROW-1890385f",
-            "ROW-f0b64094",
-            "ROW-be9526b8"
-        ]
-    },
-    "HEADER_ID": {
-        "meta": {
-            "text": "Births"
-        },
-        "type": "HEADER",
-        "id": "HEADER_ID"
-    },
-    "MARKDOWN-00178c27": {
-        "meta": {
-            "width": 5,
-            "code": "<div style=\\"text-align:center\\">\\n <h1>Birth Names Dashboard</h1>\\n <p>\\n The source dataset came from\\n <a href=\\"https://github.com/hadley/babynames\\" target=\\"_blank\\">[here]</a>\\n </p>\\n <img src=\\"/static/assets/images/babytux.jpg\\">\\n</div>\\n",
-            "height": 38
-        },
-        "type": "MARKDOWN",
-        "id": "MARKDOWN-00178c27",
-        "children": []
-    },
-    "ROOT_ID": {
-        "type": "ROOT",
-        "id": "ROOT_ID",
-        "children": [
-            "GRID_ID"
-        ]
-    },
-    "ROW-1890385f": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-1890385f",
-        "children": [
-            "CHART-e440d205",
-            "CHART-0dd270f0",
-            "CHART-a3c21bcc"
-        ]
-    },
-    "ROW-8515ace3": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-8515ace3",
-        "children": [
-            "COLUMN-25a865d6",
-            "COLUMN-4557b6ba"
-        ]
-    },
-    "ROW-be9526b8": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-be9526b8",
-        "children": [
-            "CHART-985bfd1e",
-            "CHART-17f13246",
-            "CHART-729324f6"
-        ]
-    },
-    "ROW-cc97c6ac": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-cc97c6ac",
-        "children": [
-            "CHART-976960a5",
-            "CHART-58575537"
-        ]
-    },
-    "ROW-d2e78e59": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-d2e78e59",
-        "children": [
-            "MARKDOWN-00178c27",
-            "CHART-59444e0b"
-        ]
-    },
-    "ROW-f0b64094": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-f0b64094",
-        "children": [
-            "CHART-e8774b49"
-        ]
-    },
-    "DASHBOARD_VERSION_KEY": "v2"
-}
-        """)
-    l = json.loads(js)
-    # dashboard v2 doesn't allow add markup slice
-    dash.slices = [slc for slc in slices if slc.viz_type != 'markup']
-    update_slice_ids(l, dash.slices)
-    dash.dashboard_title = "Births"
-    dash.position_json = json.dumps(l, indent=4)
-    dash.slug = "births"
-    db.session.merge(dash)
-    db.session.commit()
-
-
-def load_unicode_test_data():
-    """Loading unicode test dataset from a csv file in the repo"""
-    df = pd.read_csv(os.path.join(DATA_FOLDER, 'unicode_utf8_unixnl_test.csv'),
-                     encoding="utf-8")
-    # generate date/numeric data
-    df['dttm'] = datetime.datetime.now().date()
-    df['value'] = [random.randint(1, 100) for _ in range(len(df))]
-    df.to_sql(  # pylint: disable=no-member
-        'unicode_test',
-        db.engine,
-        if_exists='replace',
-        chunksize=500,
-        dtype={
-            'phrase': String(500),
-            'short_phrase': String(10),
-            'with_missing': String(100),
-            'dttm': Date(),
-            'value': Float(),
-        },
-        index=False)
-    print("Done loading table!")
-    print("-" * 80)
-
-    print("Creating table [unicode_test] reference")
-    obj = db.session.query(TBL).filter_by(table_name='unicode_test').first()
-    if not obj:
-        obj = TBL(table_name='unicode_test')
-    obj.main_dttm_col = 'dttm'
-    obj.database = get_or_create_main_db()
-    db.session.merge(obj)
-    db.session.commit()
-    obj.fetch_metadata()
-    tbl = obj
-
-    slice_data = {
-        "granularity_sqla": "dttm",
-        "groupby": [],
-        "metric": 'sum__value',
-        "row_limit": config.get("ROW_LIMIT"),
-        "since": "100 years ago",
-        "until": "now",
-        "where": "",
-        "viz_type": "word_cloud",
-        "size_from": "10",
-        "series": "short_phrase",
-        "size_to": "70",
-        "rotation": "square",
-        "limit": "100",
-    }
-
-    print("Creating a slice")
-    slc = Slice(
-        slice_name="Unicode Cloud",
-        viz_type='word_cloud',
-        datasource_type='table',
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-
-    print("Creating a dashboard")
-    dash = (
-        db.session.query(Dash)
-        .filter_by(dashboard_title="Unicode Test")
-        .first()
-    )
-
-    if not dash:
-        dash = Dash()
-    js = """\
-{
-    "CHART-Hkx6154FEm": {
-        "children": [],
-        "id": "CHART-Hkx6154FEm",
-        "meta": {
-            "chartId": 2225,
-            "height": 30,
-            "sliceName": "slice 1",
-            "width": 4
-        },
-        "type": "CHART"
-    },
-    "GRID_ID": {
-        "children": [
-            "ROW-SyT19EFEQ"
-        ],
-        "id": "GRID_ID",
-        "type": "GRID"
-    },
-    "ROOT_ID": {
-        "children": [
-            "GRID_ID"
-        ],
-        "id": "ROOT_ID",
-        "type": "ROOT"
-    },
-    "ROW-SyT19EFEQ": {
-        "children": [
-            "CHART-Hkx6154FEm"
-        ],
-        "id": "ROW-SyT19EFEQ",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW"
-    },
-    "DASHBOARD_VERSION_KEY": "v2"
-}
-    """
-    dash.dashboard_title = "Unicode Test"
-    l = json.loads(js)
-    update_slice_ids(l, [slc])
-    dash.position_json = json.dumps(l, indent=4)
-    dash.slug = "unicode-test"
-    dash.slices = [slc]
-    db.session.merge(dash)
-    db.session.commit()
-
-
-def load_random_time_series_data():
-    """Loading random time series data from a zip file in the repo"""
-    with gzip.open(os.path.join(DATA_FOLDER, 'random_time_series.json.gz')) as f:
-        pdf = pd.read_json(f)
-    pdf.ds = pd.to_datetime(pdf.ds, unit='s')
-    pdf.to_sql(
-        'random_time_series',
-        db.engine,
-        if_exists='replace',
-        chunksize=500,
-        dtype={
-            'ds': DateTime,
-        },
-        index=False)
-    print("Done loading table!")
-    print("-" * 80)
-
-    print("Creating table [random_time_series] reference")
-    obj = db.session.query(TBL).filter_by(table_name='random_time_series').first()
-    if not obj:
-        obj = TBL(table_name='random_time_series')
-    obj.main_dttm_col = 'ds'
-    obj.database = get_or_create_main_db()
-    db.session.merge(obj)
-    db.session.commit()
-    obj.fetch_metadata()
-    tbl = obj
-
-    slice_data = {
-        "granularity_sqla": "day",
-        "row_limit": config.get("ROW_LIMIT"),
-        "since": "1 year ago",
-        "until": "now",
-        "metric": "count",
-        "where": "",
-        "viz_type": "cal_heatmap",
-        "domain_granularity": "month",
-        "subdomain_granularity": "day",
-    }
-
-    print("Creating a slice")
-    slc = Slice(
-        slice_name="Calendar Heatmap",
-        viz_type='cal_heatmap',
-        datasource_type='table',
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-
-
-def load_country_map_data():
-    """Loading data for map with country map"""
-    csv_path = os.path.join(DATA_FOLDER, 'birth_france_data_for_country_map.csv')
-    data = pd.read_csv(csv_path, encoding="utf-8")
-    data['dttm'] = datetime.datetime.now().date()
-    data.to_sql(  # pylint: disable=no-member
-        'birth_france_by_region',
-        db.engine,
-        if_exists='replace',
-        chunksize=500,
-        dtype={
-            'DEPT_ID': String(10),
-            '2003': BigInteger,
-            '2004': BigInteger,
-            '2005': BigInteger,
-            '2006': BigInteger,
-            '2007': BigInteger,
-            '2008': BigInteger,
-            '2009': BigInteger,
-            '2010': BigInteger,
-            '2011': BigInteger,
-            '2012': BigInteger,
-            '2013': BigInteger,
-            '2014': BigInteger,
-            'dttm': Date(),
-        },
-        index=False)
-    print("Done loading table!")
-    print("-" * 80)
-    print("Creating table reference")
-    obj = db.session.query(TBL).filter_by(table_name='birth_france_by_region').first()
-    if not obj:
-        obj = TBL(table_name='birth_france_by_region')
-    obj.main_dttm_col = 'dttm'
-    obj.database = get_or_create_main_db()
-    db.session.merge(obj)
-    db.session.commit()
-    obj.fetch_metadata()
-    tbl = obj
-
-    slice_data = {
-        "granularity_sqla": "",
-        "since": "",
-        "until": "",
-        "where": "",
-        "viz_type": "country_map",
-        "entity": "DEPT_ID",
-        "metric": "avg__2004",
-        "row_limit": 500000,
-    }
-
-    print("Creating a slice")
-    slc = Slice(
-        slice_name="Birth in France by department in 2016",
-        viz_type='country_map',
-        datasource_type='table',
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    misc_dash_slices.add(slc.slice_name)
-    merge_slice(slc)
-
-
-def load_long_lat_data():
-    """Loading lat/long data from a csv file in the repo"""
-    with gzip.open(os.path.join(DATA_FOLDER, 'san_francisco.csv.gz')) as f:
-        pdf = pd.read_csv(f, encoding="utf-8")
-    start = datetime.datetime.now().replace(
-        hour=0, minute=0, second=0, microsecond=0)
-    pdf['datetime'] = [
-        start + datetime.timedelta(hours=i * 24 / (len(pdf) - 1))
-        for i in range(len(pdf))
-    ]
-    pdf['occupancy'] = [random.randint(1, 6) for _ in range(len(pdf))]
-    pdf['radius_miles'] = [random.uniform(1, 3) for _ in range(len(pdf))]
-    pdf['geohash'] = pdf[['LAT', 'LON']].apply(
-        lambda x: geohash.encode(*x), axis=1)
-    pdf['delimited'] = pdf['LAT'].map(str).str.cat(pdf['LON'].map(str), sep=',')
-    pdf.to_sql(  # pylint: disable=no-member
-        'long_lat',
-        db.engine,
-        if_exists='replace',
-        chunksize=500,
-        dtype={
-            'longitude': Float(),
-            'latitude': Float(),
-            'number': Float(),
-            'street': String(100),
-            'unit': String(10),
-            'city': String(50),
-            'district': String(50),
-            'region': String(50),
-            'postcode': Float(),
-            'id': String(100),
-            'datetime': DateTime(),
-            'occupancy': Float(),
-            'radius_miles': Float(),
-            'geohash': String(12),
-            'delimited': String(60),
-        },
-        index=False)
-    print("Done loading table!")
-    print("-" * 80)
-
-    print("Creating table reference")
-    obj = db.session.query(TBL).filter_by(table_name='long_lat').first()
-    if not obj:
-        obj = TBL(table_name='long_lat')
-    obj.main_dttm_col = 'datetime'
-    obj.database = get_or_create_main_db()
-    db.session.merge(obj)
-    db.session.commit()
-    obj.fetch_metadata()
-    tbl = obj
-
-    slice_data = {
-        "granularity_sqla": "day",
-        "since": "2014-01-01",
-        "until": "now",
-        "where": "",
-        "viz_type": "mapbox",
-        "all_columns_x": "LON",
-        "all_columns_y": "LAT",
-        "mapbox_style": "mapbox://styles/mapbox/light-v9",
-        "all_columns": ["occupancy"],
-        "row_limit": 500000,
-    }
-
-    print("Creating a slice")
-    slc = Slice(
-        slice_name="Mapbox Long/Lat",
-        viz_type='mapbox',
-        datasource_type='table',
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    misc_dash_slices.add(slc.slice_name)
-    merge_slice(slc)
-
-
-def load_multiformat_time_series_data():
-
-    """Loading time series data from a zip file in the repo"""
-    with gzip.open(os.path.join(DATA_FOLDER, 'multiformat_time_series.json.gz')) as f:
-        pdf = pd.read_json(f)
-    pdf.ds = pd.to_datetime(pdf.ds, unit='s')
-    pdf.ds2 = pd.to_datetime(pdf.ds2, unit='s')
-    pdf.to_sql(
-        'multiformat_time_series',
-        db.engine,
-        if_exists='replace',
-        chunksize=500,
-        dtype={
-            "ds": Date,
-            'ds2': DateTime,
-            "epoch_s": BigInteger,
-            "epoch_ms": BigInteger,
-            "string0": String(100),
-            "string1": String(100),
-            "string2": String(100),
-            "string3": String(100),
-        },
-        index=False)
-    print("Done loading table!")
-    print("-" * 80)
-    print("Creating table [multiformat_time_series] reference")
-    obj = db.session.query(TBL).filter_by(table_name='multiformat_time_series').first()
-    if not obj:
-        obj = TBL(table_name='multiformat_time_series')
-    obj.main_dttm_col = 'ds'
-    obj.database = get_or_create_main_db()
-    dttm_and_expr_dict = {
-        'ds': [None, None],
-        'ds2': [None, None],
-        'epoch_s': ['epoch_s', None],
-        'epoch_ms': ['epoch_ms', None],
-        'string2': ['%Y%m%d-%H%M%S', None],
-        'string1': ['%Y-%m-%d^%H:%M:%S', None],
-        'string0': ['%Y-%m-%d %H:%M:%S.%f', None],
-        'string3': ['%Y/%m/%d%H:%M:%S.%f', None],
-    }
-    for col in obj.columns:
-        dttm_and_expr = dttm_and_expr_dict[col.column_name]
-        col.python_date_format = dttm_and_expr[0]
-        col.dbatabase_expr = dttm_and_expr[1]
-        col.is_dttm = True
-    db.session.merge(obj)
-    db.session.commit()
-    obj.fetch_metadata()
-    tbl = obj
-
-    print("Creating Heatmap charts")
-    for i, col in enumerate(tbl.columns):
-        slice_data = {
-            "metrics": ['count'],
-            "granularity_sqla": col.column_name,
-            "granularity_sqla": "day",
-            "row_limit": config.get("ROW_LIMIT"),
-            "since": "1 year ago",
-            "until": "now",
-            "where": "",
-            "viz_type": "cal_heatmap",
-            "domain_granularity": "month",
-            "subdomain_granularity": "day",
-        }
-
-        slc = Slice(
-            slice_name="Calendar Heatmap multiformat " + str(i),
-            viz_type='cal_heatmap',
-            datasource_type='table',
-            datasource_id=tbl.id,
-            params=get_slice_json(slice_data),
-        )
-        merge_slice(slc)
-    misc_dash_slices.add(slc.slice_name)
-
-
-def load_misc_dashboard():
-    """Loading a dashboard featuring misc charts"""
-
-    print("Creating the dashboard")
-    db.session.expunge_all()
-    DASH_SLUG = "misc_charts"
-    dash = db.session.query(Dash).filter_by(slug=DASH_SLUG).first()
-
-    if not dash:
-        dash = Dash()
-    js = textwrap.dedent("""\
-{
-    "CHART-BkeVbh8ANQ": {
-        "children": [],
-        "id": "CHART-BkeVbh8ANQ",
-        "meta": {
-            "chartId": 4004,
-            "height": 34,
-            "sliceName": "Multi Line",
-            "width": 8
-        },
-        "type": "CHART"
-    },
-    "CHART-H1HYNzEANX": {
-        "children": [],
-        "id": "CHART-H1HYNzEANX",
-        "meta": {
-            "chartId": 3940,
-            "height": 50,
-            "sliceName": "Energy Sankey",
-            "width": 6
-        },
-        "type": "CHART"
-    },
-    "CHART-HJOYVMV0E7": {
-        "children": [],
-        "id": "CHART-HJOYVMV0E7",
-        "meta": {
-            "chartId": 3969,
-            "height": 63,
-            "sliceName": "Mapbox Long/Lat",
-            "width": 6
-        },
-        "type": "CHART"
-    },
-    "CHART-S1WYNz4AVX": {
-        "children": [],
-        "id": "CHART-S1WYNz4AVX",
-        "meta": {
-            "chartId": 3989,
-            "height": 25,
-            "sliceName": "Parallel Coordinates",
-            "width": 4
-        },
-        "type": "CHART"
-    },
-    "CHART-r19KVMNCE7": {
-        "children": [],
-        "id": "CHART-r19KVMNCE7",
-        "meta": {
-            "chartId": 3978,
-            "height": 34,
-            "sliceName": "Calendar Heatmap multiformat 7",
-            "width": 4
-        },
-        "type": "CHART"
-    },
-    "CHART-rJ4K4GV04Q": {
-        "children": [],
-        "id": "CHART-rJ4K4GV04Q",
-        "meta": {
-            "chartId": 3941,
-            "height": 63,
-            "sliceName": "Energy Force Layout",
-            "width": 6
-        },
-        "type": "CHART"
-    },
-    "CHART-rkgF4G4A4X": {
-        "children": [],
-        "id": "CHART-rkgF4G4A4X",
-        "meta": {
-            "chartId": 3970,
-            "height": 25,
-            "sliceName": "Birth in France by department in 2016",
-            "width": 8
-        },
-        "type": "CHART"
-    },
-    "CHART-rywK4GVR4X": {
-        "children": [],
-        "id": "CHART-rywK4GVR4X",
-        "meta": {
-            "chartId": 3942,
-            "height": 50,
-            "sliceName": "Heatmap",
-            "width": 6
-        },
-        "type": "CHART"
-    },
-    "COLUMN-ByUFVf40EQ": {
-        "children": [
-            "CHART-rywK4GVR4X",
-            "CHART-HJOYVMV0E7"
-        ],
-        "id": "COLUMN-ByUFVf40EQ",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT",
-            "width": 6
-        },
-        "type": "COLUMN"
-    },
-    "COLUMN-rkmYVGN04Q": {
-        "children": [
-            "CHART-rJ4K4GV04Q",
-            "CHART-H1HYNzEANX"
-        ],
-        "id": "COLUMN-rkmYVGN04Q",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT",
-            "width": 6
-        },
-        "type": "COLUMN"
-    },
-    "GRID_ID": {
-        "children": [
-            "ROW-SytNzNA4X",
-            "ROW-S1MK4M4A4X",
-            "ROW-HkFFEzVRVm"
-        ],
-        "id": "GRID_ID",
-        "type": "GRID"
-    },
-    "HEADER_ID": {
-        "id": "HEADER_ID",
-        "meta": {
-            "text": "Misc Charts"
-        },
-        "type": "HEADER"
-    },
-    "ROOT_ID": {
-        "children": [
-            "GRID_ID"
-        ],
-        "id": "ROOT_ID",
-        "type": "ROOT"
-    },
-    "ROW-HkFFEzVRVm": {
-        "children": [
-            "CHART-r19KVMNCE7",
-            "CHART-BkeVbh8ANQ"
-        ],
-        "id": "ROW-HkFFEzVRVm",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW"
-    },
-    "ROW-S1MK4M4A4X": {
-        "children": [
-            "COLUMN-rkmYVGN04Q",
-            "COLUMN-ByUFVf40EQ"
-        ],
-        "id": "ROW-S1MK4M4A4X",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW"
-    },
-    "ROW-SytNzNA4X": {
-        "children": [
-            "CHART-rkgF4G4A4X",
-            "CHART-S1WYNz4AVX"
-        ],
-        "id": "ROW-SytNzNA4X",
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW"
-    },
-    "DASHBOARD_VERSION_KEY": "v2"
-}
-    """)
-    l = json.loads(js)
-    slices = (
-        db.session
-        .query(Slice)
-        .filter(Slice.slice_name.in_(misc_dash_slices))
-        .all()
-    )
-    slices = sorted(slices, key=lambda x: x.id)
-    update_slice_ids(l, slices)
-    dash.dashboard_title = "Misc Charts"
-    dash.position_json = json.dumps(l, indent=4)
-    dash.slug = DASH_SLUG
-    dash.slices = slices
-    db.session.merge(dash)
-    db.session.commit()
-
-
-def load_deck_dash():
-    print("Loading deck.gl dashboard")
-    slices = []
-    tbl = db.session.query(TBL).filter_by(table_name='long_lat').first()
-    slice_data = {
-        "spatial": {
-            "type": "latlong",
-            "lonCol": "LON",
-            "latCol": "LAT",
-        },
-        "color_picker": {
-            "r": 205,
-            "g": 0,
-            "b": 3,
-            "a": 0.82,
-        },
-        "datasource": "5__table",
-        "filters": [],
-        "granularity_sqla": "dttm",
-        "groupby": [],
-        "having": "",
-        "mapbox_style": "mapbox://styles/mapbox/light-v9",
-        "multiplier": 10,
-        "point_radius_fixed": {"type": "metric", "value": "count"},
-        "point_unit": "square_m",
-        "min_radius": 1,
-        "row_limit": 5000,
-        "since": None,
-        "size": "count",
-        "time_grain_sqla": None,
-        "until": None,
-        "viewport": {
-            "bearing": -4.952916738791771,
-            "latitude": 37.78926922909199,
-            "longitude": -122.42613341901688,
-            "pitch": 4.750411100577438,
-            "zoom": 12.729132798697304,
-        },
-        "viz_type": "deck_scatter",
-        "where": "",
-    }
-
-    print("Creating Scatterplot slice")
-    slc = Slice(
-        slice_name="Scatterplot",
-        viz_type='deck_scatter',
-        datasource_type='table',
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    slice_data = {
-        "point_unit": "square_m",
-        "filters": [],
-        "row_limit": 5000,
-        "spatial": {
-            "type": "latlong",
-            "lonCol": "LON",
-            "latCol": "LAT",
-        },
-        "mapbox_style": "mapbox://styles/mapbox/dark-v9",
-        "granularity_sqla": "dttm",
-        "size": "count",
-        "viz_type": "deck_screengrid",
-        "since": None,
-        "point_radius": "Auto",
-        "until": None,
-        "color_picker": {
-            "a": 1,
-            "r": 14,
-            "b": 0,
-            "g": 255,
-        },
-        "grid_size": 20,
-        "where": "",
-        "having": "",
-        "viewport": {
-            "zoom": 14.161641703941438,
-            "longitude": -122.41827069521386,
-            "bearing": -4.952916738791771,
-            "latitude": 37.76024135844065,
-            "pitch": 4.750411100577438,
-        },
-        "point_radius_fixed": {"type": "fix", "value": 2000},
-        "datasource": "5__table",
-        "time_grain_sqla": None,
-        "groupby": [],
-    }
-    print("Creating Screen Grid slice")
-    slc = Slice(
-        slice_name="Screen grid",
-        viz_type='deck_screengrid',
-        datasource_type='table',
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    slice_data = {
-        "spatial": {
-            "type": "latlong",
-            "lonCol": "LON",
-            "latCol": "LAT",
-        },
-        "filters": [],
-        "row_limit": 5000,
-        "mapbox_style": "mapbox://styles/mapbox/streets-v9",
-        "granularity_sqla": "dttm",
-        "size": "count",
-        "viz_type": "deck_hex",
-        "since": None,
-        "point_radius_unit": "Pixels",
-        "point_radius": "Auto",
-        "until": None,
-        "color_picker": {
-            "a": 1,
-            "r": 14,
-            "b": 0,
-            "g": 255,
-        },
-        "grid_size": 40,
-        "extruded": True,
-        "having": "",
-        "viewport": {
-            "latitude": 37.789795085160335,
-            "pitch": 54.08961642447763,
-            "zoom": 13.835465702403654,
-            "longitude": -122.40632230075536,
-            "bearing": -2.3984797349335167,
-        },
-        "where": "",
-        "point_radius_fixed": {"type": "fix", "value": 2000},
-        "datasource": "5__table",
-        "time_grain_sqla": None,
-        "groupby": [],
-    }
-    print("Creating Hex slice")
-    slc = Slice(
-        slice_name="Hexagons",
-        viz_type='deck_hex',
-        datasource_type='table',
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    slice_data = {
-        "spatial": {
-            "type": "latlong",
-            "lonCol": "LON",
-            "latCol": "LAT",
-        },
-        "filters": [],
-        "row_limit": 5000,
-        "mapbox_style": "mapbox://styles/mapbox/satellite-streets-v9",
-        "granularity_sqla": "dttm",
-        "size": "count",
-        "viz_type": "deck_grid",
-        "point_radius_unit": "Pixels",
-        "point_radius": "Auto",
-        "time_range": "No filter",
-        "color_picker": {
-            "a": 1,
-            "r": 14,
-            "b": 0,
-            "g": 255,
-        },
-        "grid_size": 120,
-        "extruded": True,
-        "having": "",
-        "viewport": {
-            "longitude": -122.42066918995666,
-            "bearing": 155.80099696026355,
-            "zoom": 12.699690845482069,
-            "latitude": 37.7942314882596,
-            "pitch": 53.470800300695146,
-        },
-        "where": "",
-        "point_radius_fixed": {"type": "fix", "value": 2000},
-        "datasource": "5__table",
-        "time_grain_sqla": None,
-        "groupby": [],
-    }
-    print("Creating Grid slice")
-    slc = Slice(
-        slice_name="Grid",
-        viz_type='deck_grid',
-        datasource_type='table',
-        datasource_id=tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    polygon_tbl = db.session.query(TBL) \
-                    .filter_by(table_name='sf_population_polygons').first()
-    slice_data = {
-        "datasource": "11__table",
-        "viz_type": "deck_polygon",
-        "slice_id": 41,
-        "granularity_sqla": None,
-        "time_grain_sqla": None,
-        "since": None,
-        "until": None,
-        "line_column": "contour",
-        "line_type": "json",
-        "mapbox_style": "mapbox://styles/mapbox/light-v9",
-        "viewport": {
-            "longitude": -122.43388541747726,
-            "latitude": 37.752020331384834,
-            "zoom": 11.133995608594631,
-            "bearing": 37.89506450385642,
-            "pitch": 60,
-            "width": 667,
-            "height": 906,
-            "altitude": 1.5,
-            "maxZoom": 20,
-            "minZoom": 0,
-            "maxPitch": 60,
-            "minPitch": 0,
-            "maxLatitude": 85.05113,
-            "minLatitude": -85.05113
-        },
-        "reverse_long_lat": False,
-        "fill_color_picker": {
-            "r": 3,
-            "g": 65,
-            "b": 73,
-            "a": 1
-        },
-        "stroke_color_picker": {
-            "r": 0,
-            "g": 122,
-            "b": 135,
-            "a": 1
-        },
-        "filled": True,
-        "stroked": False,
-        "extruded": True,
-        "point_radius_scale": 100,
-        "js_columns": [
-            "population",
-            "area"
-        ],
-        "js_datapoint_mutator": "(d) => {\n    d.elevation = d.extraProps.population/d.extraProps.area/10\n \
-         d.fillColor = [d.extraProps.population/d.extraProps.area/60,140,0]\n \
-         return d;\n}",
-        "js_tooltip": "",
-        "js_onclick_href": "",
-        "where": "",
-        "having": "",
-        "filters": []
-    }
-
-    print("Creating Polygon slice")
-    slc = Slice(
-        slice_name="Polygons",
-        viz_type='deck_polygon',
-        datasource_type='table',
-        datasource_id=polygon_tbl.id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    slice_data = {
-        "datasource": "10__table",
-        "viz_type": "deck_arc",
-        "slice_id": 42,
-        "granularity_sqla": "dttm",
-        "time_grain_sqla": "Time Column",
-        "since": None,
-        "until": None,
-        "start_spatial": {
-            "type": "latlong",
-            "latCol": "LATITUDE",
-            "lonCol": "LONGITUDE"
-        },
-        "end_spatial": {
-            "type": "latlong",
-            "latCol": "LATITUDE_DEST",
-            "lonCol": "LONGITUDE_DEST"
-        },
-        "row_limit": 5000,
-        "mapbox_style": "mapbox://styles/mapbox/light-v9",
-        "viewport": {
-            "altitude": 1.5,
-            "bearing": 8.546256357301871,
-            "height": 642,
-            "latitude": 44.596651438714254,
-            "longitude": -91.84340711201104,
-            "maxLatitude": 85.05113,
-            "maxPitch": 60,
-            "maxZoom": 20,
-            "minLatitude": -85.05113,
-            "minPitch": 0,
-            "minZoom": 0,
-            "pitch": 60,
-            "width": 997,
-            "zoom": 2.929837070560775
-        },
-        "color_picker": {
-            "r": 0,
-            "g": 122,
-            "b": 135,
-            "a": 1
-        },
-        "stroke_width": 1,
-        "where": "",
-        "having": "",
-        "filters": []
-    }
-
-    print("Creating Arc slice")
-    slc = Slice(
-        slice_name="Arcs",
-        viz_type='deck_arc',
-        datasource_type='table',
-        datasource_id=db.session.query(TBL).filter_by(table_name='flights').first().id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    slice_data = {
-        "datasource": "12__table",
-        "slice_id": 43,
-        "viz_type": "deck_path",
-        "time_grain_sqla": "Time Column",
-        "since": None,
-        "until": None,
-        "line_column": "path_json",
-        "line_type": "json",
-        "row_limit": 5000,
-        "mapbox_style": "mapbox://styles/mapbox/light-v9",
-        "viewport": {
-            "longitude": -122.18885402582598,
-            "latitude": 37.73671752604488,
-            "zoom": 9.51847667620428,
-            "bearing": 0,
-            "pitch": 0,
-            "width": 669,
-            "height": 1094,
-            "altitude": 1.5,
-            "maxZoom": 20,
-            "minZoom": 0,
-            "maxPitch": 60,
-            "minPitch": 0,
-            "maxLatitude": 85.05113,
-            "minLatitude": -85.05113
-        },
-        "color_picker": {
-            "r": 0,
-            "g": 122,
-            "b": 135,
-            "a": 1
-        },
-        "line_width": 150,
-        "reverse_long_lat": False,
-        "js_columns": [
-            "color"
-        ],
-        "js_datapoint_mutator": "d => {\n    return {\n        ...d,\n        color: \
-            colors.hexToRGB(d.extraProps.color),\n    }\n}",
-        "js_tooltip": "",
-        "js_onclick_href": "",
-        "where": "",
-        "having": "",
-        "filters": []
-    }
-
-    print("Creating Path slice")
-    slc = Slice(
-        slice_name="Path",
-        viz_type='deck_path',
-        datasource_type='table',
-        datasource_id=db.session.query(TBL).filter_by(table_name='bart_lines').first().id,
-        params=get_slice_json(slice_data),
-    )
-    merge_slice(slc)
-    slices.append(slc)
-
-    print("Creating a dashboard")
-    title = "deck.gl Demo"
-    dash = db.session.query(Dash).filter_by(dashboard_title=title).first()
-
-    if not dash:
-        dash = Dash()
-    js = textwrap.dedent("""\
-{
-    "CHART-3afd9d70": {
-        "meta": {
-            "chartId": 66,
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-3afd9d70",
-        "children": []
-    },
-    "CHART-2ee7fa5e": {
-        "meta": {
-            "chartId": 67,
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-2ee7fa5e",
-        "children": []
-    },
-    "CHART-201f7715": {
-        "meta": {
-            "chartId": 68,
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-201f7715",
-        "children": []
-    },
-    "CHART-d02f6c40": {
-        "meta": {
-            "chartId": 69,
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-d02f6c40",
-        "children": []
-    },
-    "CHART-2673431d": {
-        "meta": {
-            "chartId": 70,
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-2673431d",
-        "children": []
-    },
-    "CHART-85265a60": {
-        "meta": {
-            "chartId": 71,
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-85265a60",
-        "children": []
-    },
-    "CHART-2b87513c": {
-        "meta": {
-            "chartId": 72,
-            "width": 6,
-            "height": 50
-        },
-        "type": "CHART",
-        "id": "CHART-2b87513c",
-        "children": []
-    },
-    "GRID_ID": {
-        "type": "GRID",
-        "id": "GRID_ID",
-        "children": [
-            "ROW-a7b16cb5",
-            "ROW-72c218a5",
-            "ROW-957ba55b",
-            "ROW-af041bdd"
-        ]
-    },
-    "HEADER_ID": {
-        "meta": {
-            "text": "deck.gl Demo"
-        },
-        "type": "HEADER",
-        "id": "HEADER_ID"
-    },
-    "ROOT_ID": {
-        "type": "ROOT",
-        "id": "ROOT_ID",
-        "children": [
-            "GRID_ID"
-        ]
-    },
-    "ROW-72c218a5": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-72c218a5",
-        "children": [
-            "CHART-d02f6c40",
-            "CHART-201f7715"
-        ]
-    },
-    "ROW-957ba55b": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-957ba55b",
-        "children": [
-            "CHART-2673431d",
-            "CHART-85265a60"
-        ]
-    },
-    "ROW-a7b16cb5": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-a7b16cb5",
-        "children": [
-            "CHART-3afd9d70",
-            "CHART-2ee7fa5e"
-        ]
-    },
-    "ROW-af041bdd": {
-        "meta": {
-            "background": "BACKGROUND_TRANSPARENT"
-        },
-        "type": "ROW",
-        "id": "ROW-af041bdd",
-        "children": [
-            "CHART-2b87513c"
-        ]
-    },
-    "DASHBOARD_VERSION_KEY": "v2"
-}
-    """)
-    l = json.loads(js)
-    update_slice_ids(l, slices)
-    dash.dashboard_title = title
-    dash.position_json = json.dumps(l, indent=4)
-    dash.slug = "deck"
-    dash.slices = slices
-    db.session.merge(dash)
-    db.session.commit()
-
-
-def load_flights():
-    """Loading random time series data from a zip file in the repo"""
-    tbl_name = 'flights'
-    with gzip.open(os.path.join(DATA_FOLDER, 'fligth_data.csv.gz')) as f:
-        pdf = pd.read_csv(f, encoding='latin-1')
-
-    # Loading airports info to join and get lat/long
-    with gzip.open(os.path.join(DATA_FOLDER, 'airports.csv.gz')) as f:
-        airports = pd.read_csv(f, encoding='latin-1')
-    airports = airports.set_index('IATA_CODE')
-
-    pdf['ds'] = pdf.YEAR.map(str) + '-0' + pdf.MONTH.map(str) + '-0' + pdf.DAY.map(str)
-    pdf.ds = pd.to_datetime(pdf.ds)
-    del pdf['YEAR']
-    del pdf['MONTH']
-    del pdf['DAY']
-
-    pdf = pdf.join(airports, on='ORIGIN_AIRPORT', rsuffix='_ORIG')
-    pdf = pdf.join(airports, on='DESTINATION_AIRPORT', rsuffix='_DEST')
-    pdf.to_sql(
-        tbl_name,
-        db.engine,
-        if_exists='replace',
-        chunksize=500,
-        dtype={
-            'ds': DateTime,
-        },
-        index=False)
-    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
-    if not tbl:
-        tbl = TBL(table_name=tbl_name)
-    tbl.description = "Random set of flights in the US"
-    tbl.database = get_or_create_main_db()
-    db.session.merge(tbl)
-    db.session.commit()
-    tbl.fetch_metadata()
-    print("Done loading table!")
-
-
-def load_paris_iris_geojson():
-    tbl_name = 'paris_iris_mapping'
-
-    with gzip.open(os.path.join(DATA_FOLDER, 'paris_iris.json.gz')) as f:
-        df = pd.read_json(f)
-        df['features'] = df.features.map(json.dumps)
-
-    df.to_sql(
-        tbl_name,
-        db.engine,
-        if_exists='replace',
-        chunksize=500,
-        dtype={
-            'color': String(255),
-            'name': String(255),
-            'features': Text,
-            'type': Text,
-        },
-        index=False)
-    print("Creating table {} reference".format(tbl_name))
-    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
-    if not tbl:
-        tbl = TBL(table_name=tbl_name)
-    tbl.description = "Map of Paris"
-    tbl.database = get_or_create_main_db()
-    db.session.merge(tbl)
-    db.session.commit()
-    tbl.fetch_metadata()
-
-
-def load_sf_population_polygons():
-    tbl_name = 'sf_population_polygons'
-
-    with gzip.open(os.path.join(DATA_FOLDER, 'sf_population.json.gz')) as f:
-        df = pd.read_json(f)
-        df['contour'] = df.contour.map(json.dumps)
-
-    df.to_sql(
-        tbl_name,
-        db.engine,
-        if_exists='replace',
-        chunksize=500,
-        dtype={
-            'zipcode': BigInteger,
-            'population': BigInteger,
-            'contour': Text,
-            'area': BigInteger,
-        },
-        index=False)
-    print("Creating table {} reference".format(tbl_name))
-    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
-    if not tbl:
-        tbl = TBL(table_name=tbl_name)
-    tbl.description = "Population density of San Francisco"
-    tbl.database = get_or_create_main_db()
-    db.session.merge(tbl)
-    db.session.commit()
-    tbl.fetch_metadata()
-
-
-def load_bart_lines():
-    tbl_name = 'bart_lines'
-    with gzip.open(os.path.join(DATA_FOLDER, 'bart-lines.json.gz')) as f:
-        df = pd.read_json(f, encoding='latin-1')
-        df['path_json'] = df.path.map(json.dumps)
-        df['polyline'] = df.path.map(polyline.encode)
-        del df['path']
-    df.to_sql(
-        tbl_name,
-        db.engine,
-        if_exists='replace',
-        chunksize=500,
-        dtype={
-            'color': String(255),
-            'name': String(255),
-            'polyline': Text,
-            'path_json': Text,
-        },
-        index=False)
-    print("Creating table {} reference".format(tbl_name))
-    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
-    if not tbl:
-        tbl = TBL(table_name=tbl_name)
-    tbl.description = "BART lines"
-    tbl.database = get_or_create_main_db()
-    db.session.merge(tbl)
-    db.session.commit()
-    tbl.fetch_metadata()
-
-
-def load_multi_line():
-    load_world_bank_health_n_pop()
-    load_birth_names()
-    ids = [
-        row.id for row in
-        db.session.query(Slice).filter(
-            Slice.slice_name.in_(['Growth Rate', 'Trends']))
-    ]
-
-    slc = Slice(
-        datasource_type='table',  # not true, but needed
-        datasource_id=1,          # cannot be empty
-        slice_name="Multi Line",
-        viz_type='line_multi',
-        params=json.dumps({
-            "slice_name": "Multi Line",
-            "viz_type": "line_multi",
-            "line_charts": [ids[0]],
-            "line_charts_2": [ids[1]],
-            "since": "1960-01-01",
-            "prefix_metric_with_slice_name": True,
-        }),
-    )
-
-    misc_dash_slices.add(slc.slice_name)
-    merge_slice(slc)
+from .bart_lines import load_bart_lines  # noqa
+from .birth_names import load_birth_names  # noqa
+from .country_map import load_country_map_data  # noqa
+from .css_templates import load_css_templates  # noqa
+from .deck import load_deck_dash  # noqa
+from .energy import load_energy  # noqa
+from .flights import load_flights  # noqa
+from .long_lat import load_long_lat_data  # noqa
+from .misc_dashboard import load_misc_dashboard  # noqa
+from .multi_line import load_multi_line  # noqa
+from .multiformat_time_series import load_multiformat_time_series  # noqa
+from .paris import load_paris_iris_geojson  # noqa
+from .random_time_series import load_random_time_series_data  # noqa
+from .sf_population_polygons import load_sf_population_polygons  # noqa
+from .unicode_test_data import load_unicode_test_data  # noqa
+from .world_bank import load_world_bank_health_n_pop  # noqa
diff --git a/superset/data/bart_lines.py b/superset/data/bart_lines.py
new file mode 100644
index 000000000..8ae8cf441
--- /dev/null
+++ b/superset/data/bart_lines.py
@@ -0,0 +1,41 @@
+import gzip
+import json
+import os
+
+import pandas as pd
+import polyline
+from sqlalchemy import String, Text
+
+from superset import db
+from superset.utils.core import get_or_create_main_db
+from .helpers import DATA_FOLDER, TBL
+
+
+def load_bart_lines():
+    tbl_name = 'bart_lines'
+    with gzip.open(os.path.join(DATA_FOLDER, 'bart-lines.json.gz')) as f:
+        df = pd.read_json(f, encoding='latin-1')
+        df['path_json'] = df.path.map(json.dumps)
+        df['polyline'] = df.path.map(polyline.encode)
+        del df['path']
+    df.to_sql(
+        tbl_name,
+        db.engine,
+        if_exists='replace',
+        chunksize=500,
+        dtype={
+            'color': String(255),
+            'name': String(255),
+            'polyline': Text,
+            'path_json': Text,
+        },
+        index=False)
+    print('Creating table {} reference'.format(tbl_name))
+    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
+    if not tbl:
+        tbl = TBL(table_name=tbl_name)
+    tbl.description = 'BART lines'
+    tbl.database = get_or_create_main_db()
+    db.session.merge(tbl)
+    db.session.commit()
+    tbl.fetch_metadata()
diff --git a/superset/data/birth_names.py b/superset/data/birth_names.py
new file mode 100644
index 000000000..b697c3198
--- /dev/null
+++ b/superset/data/birth_names.py
@@ -0,0 +1,588 @@
+import gzip
+import json
+import os
+import textwrap
+
+import pandas as pd
+from sqlalchemy import DateTime, String
+
+from superset import db
+from superset.connectors.sqla.models import TableColumn
+from superset.utils.core import get_or_create_main_db
+from .helpers import (
+    config,
+    Dash,
+    DATA_FOLDER,
+    get_slice_json,
+    merge_slice,
+    Slice,
+    TBL,
+    update_slice_ids,
+)
+
+
+def load_birth_names():
+    """Loading birth name dataset from a zip file in the repo"""
+    with gzip.open(os.path.join(DATA_FOLDER, 'birth_names.json.gz')) as f:
+        pdf = pd.read_json(f)
+    pdf.ds = pd.to_datetime(pdf.ds, unit='ms')
+    pdf.to_sql(
+        'birth_names',
+        db.engine,
+        if_exists='replace',
+        chunksize=500,
+        dtype={
+            'ds': DateTime,
+            'gender': String(16),
+            'state': String(10),
+            'name': String(255),
+        },
+        index=False)
+    print('Done loading table!')
+    print('-' * 80)
+
+    print('Creating table [birth_names] reference')
+    obj = db.session.query(TBL).filter_by(table_name='birth_names').first()
+    if not obj:
+        obj = TBL(table_name='birth_names')
+    obj.main_dttm_col = 'ds'
+    obj.database = get_or_create_main_db()
+    obj.filter_select_enabled = True
+
+    if not any(col.column_name == 'num_california' for col in obj.columns):
+        obj.columns.append(TableColumn(
+            column_name='num_california',
+            expression="CASE WHEN state = 'CA' THEN num ELSE 0 END",
+        ))
+
+    db.session.merge(obj)
+    db.session.commit()
+    obj.fetch_metadata()
+    tbl = obj
+
+    defaults = {
+        'compare_lag': '10',
+        'compare_suffix': 'o10Y',
+        'limit': '25',
+        'granularity_sqla': 'ds',
+        'groupby': [],
+        'metric': 'sum__num',
+        'metrics': ['sum__num'],
+        'row_limit': config.get('ROW_LIMIT'),
+        'since': '100 years ago',
+        'until': 'now',
+        'viz_type': 'table',
+        'where': '',
+        'markup_type': 'markdown',
+    }
+
+    print('Creating some slices')
+    slices = [
+        Slice(
+            slice_name='Girls',
+            viz_type='table',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                groupby=['name'],
+                filters=[{
+                    'col': 'gender',
+                    'op': 'in',
+                    'val': ['girl'],
+                }],
+                row_limit=50,
+                timeseries_limit_metric='sum__num')),
+        Slice(
+            slice_name='Boys',
+            viz_type='table',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                groupby=['name'],
+                filters=[{
+                    'col': 'gender',
+                    'op': 'in',
+                    'val': ['boy'],
+                }],
+                row_limit=50)),
+        Slice(
+            slice_name='Participants',
+            viz_type='big_number',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='big_number', granularity_sqla='ds',
+                compare_lag='5', compare_suffix='over 5Y')),
+        Slice(
+            slice_name='Genders',
+            viz_type='pie',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='pie', groupby=['gender'])),
+        Slice(
+            slice_name='Genders by State',
+            viz_type='dist_bar',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                adhoc_filters=[
+                    {
+                        'clause': 'WHERE',
+                        'expressionType': 'SIMPLE',
+                        'filterOptionName': '2745eae5',
+                        'comparator': ['other'],
+                        'operator': 'not in',
+                        'subject': 'state',
+                    },
+                ],
+                viz_type='dist_bar',
+                metrics=[
+                    {
+                        'expressionType': 'SIMPLE',
+                        'column': {
+                            'column_name': 'sum_boys',
+                            'type': 'BIGINT(20)',
+                        },
+                        'aggregate': 'SUM',
+                        'label': 'Boys',
+                        'optionName': 'metric_11',
+                    },
+                    {
+                        'expressionType': 'SIMPLE',
+                        'column': {
+                            'column_name': 'sum_girls',
+                            'type': 'BIGINT(20)',
+                        },
+                        'aggregate': 'SUM',
+                        'label': 'Girls',
+                        'optionName': 'metric_12',
+                    },
+                ],
+                groupby=['state'])),
+        Slice(
+            slice_name='Trends',
+            viz_type='line',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='line', groupby=['name'],
+                granularity_sqla='ds', rich_tooltip=True, show_legend=True)),
+        Slice(
+            slice_name='Average and Sum Trends',
+            viz_type='dual_line',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='dual_line',
+                metric={
+                    'expressionType': 'SIMPLE',
+                    'column': {
+                        'column_name': 'num',
+                        'type': 'BIGINT(20)',
+                    },
+                    'aggregate': 'AVG',
+                    'label': 'AVG(num)',
+                    'optionName': 'metric_vgops097wej_g8uff99zhk7',
+                },
+                metric_2='sum__num',
+                granularity_sqla='ds')),
+        Slice(
+            slice_name='Title',
+            viz_type='markup',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='markup', markup_type='html',
+                code="""\
+    <div style='text-align:center'>
+        <h1>Birth Names Dashboard</h1>
+        <p>
+            The source dataset came from
+            <a href='https://github.com/hadley/babynames' target='_blank'>[here]</a>
+        </p>
+        <img src='/static/assets/images/babytux.jpg'>
+    </div>
+    """)),
+        Slice(
+            slice_name='Name Cloud',
+            viz_type='word_cloud',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='word_cloud', size_from='10',
+                series='name', size_to='70', rotation='square',
+                limit='100')),
+        Slice(
+            slice_name='Pivot Table',
+            viz_type='pivot_table',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='pivot_table', metrics=['sum__num'],
+                groupby=['name'], columns=['state'])),
+        Slice(
+            slice_name='Number of Girls',
+            viz_type='big_number_total',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='big_number_total', granularity_sqla='ds',
+                filters=[{
+                    'col': 'gender',
+                    'op': 'in',
+                    'val': ['girl'],
+                }],
+                subheader='total female participants')),
+        Slice(
+            slice_name='Number of California Births',
+            viz_type='big_number_total',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                metric={
+                    'expressionType': 'SIMPLE',
+                    'column': {
+                        'column_name': 'num_california',
+                        'expression': "CASE WHEN state = 'CA' THEN num ELSE 0 END",
+                    },
+                    'aggregate': 'SUM',
+                    'label': 'SUM(num_california)',
+                },
+                viz_type='big_number_total',
+                granularity_sqla='ds')),
+        Slice(
+            slice_name='Top 10 California Names Timeseries',
+            viz_type='line',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                metrics=[{
+                    'expressionType': 'SIMPLE',
+                    'column': {
+                        'column_name': 'num_california',
+                        'expression': "CASE WHEN state = 'CA' THEN num ELSE 0 END",
+                    },
+                    'aggregate': 'SUM',
+                    'label': 'SUM(num_california)',
+                }],
+                viz_type='line',
+                granularity_sqla='ds',
+                groupby=['name'],
+                timeseries_limit_metric={
+                    'expressionType': 'SIMPLE',
+                    'column': {
+                        'column_name': 'num_california',
+                        'expression': "CASE WHEN state = 'CA' THEN num ELSE 0 END",
+                    },
+                    'aggregate': 'SUM',
+                    'label': 'SUM(num_california)',
+                },
+                limit='10')),
+        Slice(
+            slice_name='Names Sorted by Num in California',
+            viz_type='table',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                groupby=['name'],
+                row_limit=50,
+                timeseries_limit_metric={
+                    'expressionType': 'SIMPLE',
+                    'column': {
+                        'column_name': 'num_california',
+                        'expression': "CASE WHEN state = 'CA' THEN num ELSE 0 END",
+                    },
+                    'aggregate': 'SUM',
+                    'label': 'SUM(num_california)',
+                })),
+        Slice(
+            slice_name='Num Births Trend',
+            viz_type='line',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='line')),
+    ]
+    for slc in slices:
+        merge_slice(slc)
+
+    print('Creating a dashboard')
+    dash = db.session.query(Dash).filter_by(dashboard_title='Births').first()
+
+    if not dash:
+        dash = Dash()
+    js = textwrap.dedent("""\
+{
+    "CHART-0dd270f0": {
+        "meta": {
+            "chartId": 51,
+            "width": 2,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-0dd270f0",
+        "children": []
+    },
+    "CHART-a3c21bcc": {
+        "meta": {
+            "chartId": 52,
+            "width": 2,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-a3c21bcc",
+        "children": []
+    },
+    "CHART-976960a5": {
+        "meta": {
+            "chartId": 53,
+            "width": 2,
+            "height": 25
+        },
+        "type": "CHART",
+        "id": "CHART-976960a5",
+        "children": []
+    },
+    "CHART-58575537": {
+        "meta": {
+            "chartId": 54,
+            "width": 2,
+            "height": 25
+        },
+        "type": "CHART",
+        "id": "CHART-58575537",
+        "children": []
+    },
+    "CHART-e9cd8f0b": {
+        "meta": {
+            "chartId": 55,
+            "width": 8,
+            "height": 38
+        },
+        "type": "CHART",
+        "id": "CHART-e9cd8f0b",
+        "children": []
+    },
+    "CHART-e440d205": {
+        "meta": {
+            "chartId": 56,
+            "width": 8,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-e440d205",
+        "children": []
+    },
+    "CHART-59444e0b": {
+        "meta": {
+            "chartId": 57,
+            "width": 3,
+            "height": 38
+        },
+        "type": "CHART",
+        "id": "CHART-59444e0b",
+        "children": []
+    },
+    "CHART-e2cb4997": {
+        "meta": {
+            "chartId": 59,
+            "width": 4,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-e2cb4997",
+        "children": []
+    },
+    "CHART-e8774b49": {
+        "meta": {
+            "chartId": 60,
+            "width": 12,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-e8774b49",
+        "children": []
+    },
+    "CHART-985bfd1e": {
+        "meta": {
+            "chartId": 61,
+            "width": 4,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-985bfd1e",
+        "children": []
+    },
+    "CHART-17f13246": {
+        "meta": {
+            "chartId": 62,
+            "width": 4,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-17f13246",
+        "children": []
+    },
+    "CHART-729324f6": {
+        "meta": {
+            "chartId": 63,
+            "width": 4,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-729324f6",
+        "children": []
+    },
+    "COLUMN-25a865d6": {
+        "meta": {
+            "width": 4,
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "COLUMN",
+        "id": "COLUMN-25a865d6",
+        "children": [
+            "ROW-cc97c6ac",
+            "CHART-e2cb4997"
+        ]
+    },
+    "COLUMN-4557b6ba": {
+        "meta": {
+            "width": 8,
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "COLUMN",
+        "id": "COLUMN-4557b6ba",
+        "children": [
+            "ROW-d2e78e59",
+            "CHART-e9cd8f0b"
+        ]
+    },
+    "GRID_ID": {
+        "type": "GRID",
+        "id": "GRID_ID",
+        "children": [
+            "ROW-8515ace3",
+            "ROW-1890385f",
+            "ROW-f0b64094",
+            "ROW-be9526b8"
+        ]
+    },
+    "HEADER_ID": {
+        "meta": {
+            "text": "Births"
+        },
+        "type": "HEADER",
+        "id": "HEADER_ID"
+    },
+    "MARKDOWN-00178c27": {
+        "meta": {
+            "width": 5,
+            "code": "<div style=\\"text-align:center\\">\\n <h1>Birth Names Dashboard</h1>\\n <p>\\n The source dataset came from\\n <a href=\\"https://github.com/hadley/babynames\\" target=\\"_blank\\">[here]</a>\\n </p>\\n <img src=\\"/static/assets/images/babytux.jpg\\">\\n</div>\\n",
+            "height": 38
+        },
+        "type": "MARKDOWN",
+        "id": "MARKDOWN-00178c27",
+        "children": []
+    },
+    "ROOT_ID": {
+        "type": "ROOT",
+        "id": "ROOT_ID",
+        "children": [
+            "GRID_ID"
+        ]
+    },
+    "ROW-1890385f": {
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW",
+        "id": "ROW-1890385f",
+        "children": [
+            "CHART-e440d205",
+            "CHART-0dd270f0",
+            "CHART-a3c21bcc"
+        ]
+    },
+    "ROW-8515ace3": {
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW",
+        "id": "ROW-8515ace3",
+        "children": [
+            "COLUMN-25a865d6",
+            "COLUMN-4557b6ba"
+        ]
+    },
+    "ROW-be9526b8": {
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW",
+        "id": "ROW-be9526b8",
+        "children": [
+            "CHART-985bfd1e",
+            "CHART-17f13246",
+            "CHART-729324f6"
+        ]
+    },
+    "ROW-cc97c6ac": {
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW",
+        "id": "ROW-cc97c6ac",
+        "children": [
+            "CHART-976960a5",
+            "CHART-58575537"
+        ]
+    },
+    "ROW-d2e78e59": {
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW",
+        "id": "ROW-d2e78e59",
+        "children": [
+            "MARKDOWN-00178c27",
+            "CHART-59444e0b"
+        ]
+    },
+    "ROW-f0b64094": {
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW",
+        "id": "ROW-f0b64094",
+        "children": [
+            "CHART-e8774b49"
+        ]
+    },
+    "DASHBOARD_VERSION_KEY": "v2"
+}
+        """)
+    pos = json.loads(js)
+    # dashboard v2 doesn't allow add markup slice
+    dash.slices = [slc for slc in slices if slc.viz_type != 'markup']
+    update_slice_ids(pos, dash.slices)
+    dash.dashboard_title = 'Births'
+    dash.position_json = json.dumps(pos, indent=4)
+    dash.slug = 'births'
+    db.session.merge(dash)
+    db.session.commit()
diff --git a/superset/data/country_map.py b/superset/data/country_map.py
new file mode 100644
index 000000000..6b32abed9
--- /dev/null
+++ b/superset/data/country_map.py
@@ -0,0 +1,79 @@
+import datetime
+import os
+
+import pandas as pd
+from sqlalchemy import BigInteger, Date, String
+
+from superset import db
+from superset.utils import core as utils
+from .helpers import (
+    DATA_FOLDER,
+    get_slice_json,
+    merge_slice,
+    misc_dash_slices,
+    Slice,
+    TBL,
+)
+
+
+def load_country_map_data():
+    """Loading data for map with country map"""
+    csv_path = os.path.join(DATA_FOLDER, 'birth_france_data_for_country_map.csv')
+    data = pd.read_csv(csv_path, encoding='utf-8')
+    data['dttm'] = datetime.datetime.now().date()
+    data.to_sql(  # pylint: disable=no-member
+        'birth_france_by_region',
+        db.engine,
+        if_exists='replace',
+        chunksize=500,
+        dtype={
+            'DEPT_ID': String(10),
+            '2003': BigInteger,
+            '2004': BigInteger,
+            '2005': BigInteger,
+            '2006': BigInteger,
+            '2007': BigInteger,
+            '2008': BigInteger,
+            '2009': BigInteger,
+            '2010': BigInteger,
+            '2011': BigInteger,
+            '2012': BigInteger,
+            '2013': BigInteger,
+            '2014': BigInteger,
+            'dttm': Date(),
+        },
+        index=False)
+    print('Done loading table!')
+    print('-' * 80)
+    print('Creating table reference')
+    obj = db.session.query(TBL).filter_by(table_name='birth_france_by_region').first()
+    if not obj:
+        obj = TBL(table_name='birth_france_by_region')
+    obj.main_dttm_col = 'dttm'
+    obj.database = utils.get_or_create_main_db()
+    db.session.merge(obj)
+    db.session.commit()
+    obj.fetch_metadata()
+    tbl = obj
+
+    slice_data = {
+        'granularity_sqla': '',
+        'since': '',
+        'until': '',
+        'where': '',
+        'viz_type': 'country_map',
+        'entity': 'DEPT_ID',
+        'metric': 'avg__2004',
+        'row_limit': 500000,
+    }
+
+    print('Creating a slice')
+    slc = Slice(
+        slice_name='Birth in France by department in 2016',
+        viz_type='country_map',
+        datasource_type='table',
+        datasource_id=tbl.id,
+        params=get_slice_json(slice_data),
+    )
+    misc_dash_slices.add(slc.slice_name)
+    merge_slice(slc)
diff --git a/superset/data/css_templates.py b/superset/data/css_templates.py
new file mode 100644
index 000000000..a99173671
--- /dev/null
+++ b/superset/data/css_templates.py
@@ -0,0 +1,103 @@
+import textwrap
+
+from superset import db
+from superset.models.core import CssTemplate
+
+
+def load_css_templates():
+    """Loads 2 css templates to demonstrate the feature"""
+    print('Creating default CSS templates')
+
+    obj = db.session.query(CssTemplate).filter_by(template_name='Flat').first()
+    if not obj:
+        obj = CssTemplate(template_name='Flat')
+    css = textwrap.dedent("""\
+    .gridster div.widget {
+        transition: background-color 0.5s ease;
+        background-color: #FAFAFA;
+        border: 1px solid #CCC;
+        box-shadow: none;
+        border-radius: 0px;
+    }
+    .gridster div.widget:hover {
+        border: 1px solid #000;
+        background-color: #EAEAEA;
+    }
+    .navbar {
+        transition: opacity 0.5s ease;
+        opacity: 0.05;
+    }
+    .navbar:hover {
+        opacity: 1;
+    }
+    .chart-header .header{
+        font-weight: normal;
+        font-size: 12px;
+    }
+    /*
+    var bnbColors = [
+        //rausch    hackb      kazan      babu      lima        beach     tirol
+        '#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
+        '#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
+        '#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
+     ];
+    */
+    """)
+    obj.css = css
+    db.session.merge(obj)
+    db.session.commit()
+
+    obj = (
+        db.session.query(CssTemplate).filter_by(template_name='Courier Black').first())
+    if not obj:
+        obj = CssTemplate(template_name='Courier Black')
+    css = textwrap.dedent("""\
+    .gridster div.widget {
+        transition: background-color 0.5s ease;
+        background-color: #EEE;
+        border: 2px solid #444;
+        border-radius: 15px;
+        box-shadow: none;
+    }
+    h2 {
+        color: white;
+        font-size: 52px;
+    }
+    .navbar {
+        box-shadow: none;
+    }
+    .gridster div.widget:hover {
+        border: 2px solid #000;
+        background-color: #EAEAEA;
+    }
+    .navbar {
+        transition: opacity 0.5s ease;
+        opacity: 0.05;
+    }
+    .navbar:hover {
+        opacity: 1;
+    }
+    .chart-header .header{
+        font-weight: normal;
+        font-size: 12px;
+    }
+    .nvd3 text {
+        font-size: 12px;
+        font-family: inherit;
+    }
+    body{
+        background: #000;
+        font-family: Courier, Monaco, monospace;;
+    }
+    /*
+    var bnbColors = [
+        //rausch    hackb      kazan      babu      lima        beach     tirol
+        '#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
+        '#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
+        '#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
+     ];
+    */
+    """)
+    obj.css = css
+    db.session.merge(obj)
+    db.session.commit()
diff --git a/superset/data/deck.py b/superset/data/deck.py
new file mode 100644
index 000000000..3307e9ede
--- /dev/null
+++ b/superset/data/deck.py
@@ -0,0 +1,559 @@
+# pylint: disable=too-many-statements
+import json
+
+from superset import db
+from .helpers import (
+    Dash,
+    get_slice_json,
+    merge_slice,
+    Slice,
+    TBL,
+    update_slice_ids,
+)
+
+COLOR_RED = {
+    'r': 205,
+    'g': 0,
+    'b': 3,
+    'a': 0.82,
+}
+POSITION_JSON = """\
+{
+    "CHART-3afd9d70": {
+        "meta": {
+            "chartId": 66,
+            "width": 6,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-3afd9d70",
+        "children": []
+    },
+    "CHART-2ee7fa5e": {
+        "meta": {
+            "chartId": 67,
+            "width": 6,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-2ee7fa5e",
+        "children": []
+    },
+    "CHART-201f7715": {
+        "meta": {
+            "chartId": 68,
+            "width": 6,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-201f7715",
+        "children": []
+    },
+    "CHART-d02f6c40": {
+        "meta": {
+            "chartId": 69,
+            "width": 6,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-d02f6c40",
+        "children": []
+    },
+    "CHART-2673431d": {
+        "meta": {
+            "chartId": 70,
+            "width": 6,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-2673431d",
+        "children": []
+    },
+    "CHART-85265a60": {
+        "meta": {
+            "chartId": 71,
+            "width": 6,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-85265a60",
+        "children": []
+    },
+    "CHART-2b87513c": {
+        "meta": {
+            "chartId": 72,
+            "width": 6,
+            "height": 50
+        },
+        "type": "CHART",
+        "id": "CHART-2b87513c",
+        "children": []
+    },
+    "GRID_ID": {
+        "type": "GRID",
+        "id": "GRID_ID",
+        "children": [
+            "ROW-a7b16cb5",
+            "ROW-72c218a5",
+            "ROW-957ba55b",
+            "ROW-af041bdd"
+        ]
+    },
+    "HEADER_ID": {
+        "meta": {
+            "text": "deck.gl Demo"
+        },
+        "type": "HEADER",
+        "id": "HEADER_ID"
+    },
+    "ROOT_ID": {
+        "type": "ROOT",
+        "id": "ROOT_ID",
+        "children": [
+            "GRID_ID"
+        ]
+    },
+    "ROW-72c218a5": {
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW",
+        "id": "ROW-72c218a5",
+        "children": [
+            "CHART-d02f6c40",
+            "CHART-201f7715"
+        ]
+    },
+    "ROW-957ba55b": {
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW",
+        "id": "ROW-957ba55b",
+        "children": [
+            "CHART-2673431d",
+            "CHART-85265a60"
+        ]
+    },
+    "ROW-a7b16cb5": {
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW",
+        "id": "ROW-a7b16cb5",
+        "children": [
+            "CHART-3afd9d70",
+            "CHART-2ee7fa5e"
+        ]
+    },
+    "ROW-af041bdd": {
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW",
+        "id": "ROW-af041bdd",
+        "children": [
+            "CHART-2b87513c"
+        ]
+    },
+    "DASHBOARD_VERSION_KEY": "v2"
+}"""
+
+
+def load_deck_dash():
+    print('Loading deck.gl dashboard')
+    slices = []
+    tbl = db.session.query(TBL).filter_by(table_name='long_lat').first()
+    slice_data = {
+        'spatial': {
+            'type': 'latlong',
+            'lonCol': 'LON',
+            'latCol': 'LAT',
+        },
+        'color_picker': COLOR_RED,
+        'datasource': '5__table',
+        'filters': [],
+        'granularity_sqla': None,
+        'groupby': [],
+        'having': '',
+        'mapbox_style': 'mapbox://styles/mapbox/light-v9',
+        'multiplier': 10,
+        'point_radius_fixed': {'type': 'metric', 'value': 'count'},
+        'point_unit': 'square_m',
+        'min_radius': 1,
+        'row_limit': 5000,
+        'time_range': ' : ',
+        'size': 'count',
+        'time_grain_sqla': None,
+        'viewport': {
+            'bearing': -4.952916738791771,
+            'latitude': 37.78926922909199,
+            'longitude': -122.42613341901688,
+            'pitch': 4.750411100577438,
+            'zoom': 12.729132798697304,
+        },
+        'viz_type': 'deck_scatter',
+        'where': '',
+    }
+
+    print('Creating Scatterplot slice')
+    slc = Slice(
+        slice_name='Scatterplot',
+        viz_type='deck_scatter',
+        datasource_type='table',
+        datasource_id=tbl.id,
+        params=get_slice_json(slice_data),
+    )
+    merge_slice(slc)
+    slices.append(slc)
+
+    slice_data = {
+        'point_unit': 'square_m',
+        'filters': [],
+        'row_limit': 5000,
+        'spatial': {
+            'type': 'latlong',
+            'lonCol': 'LON',
+            'latCol': 'LAT',
+        },
+        'mapbox_style': 'mapbox://styles/mapbox/dark-v9',
+        'granularity_sqla': None,
+        'size': 'count',
+        'viz_type': 'deck_screengrid',
+        'time_range': 'No filter',
+        'point_radius': 'Auto',
+        'color_picker': {
+            'a': 1,
+            'r': 14,
+            'b': 0,
+            'g': 255,
+        },
+        'grid_size': 20,
+        'where': '',
+        'having': '',
+        'viewport': {
+            'zoom': 14.161641703941438,
+            'longitude': -122.41827069521386,
+            'bearing': -4.952916738791771,
+            'latitude': 37.76024135844065,
+            'pitch': 4.750411100577438,
+        },
+        'point_radius_fixed': {'type': 'fix', 'value': 2000},
+        'datasource': '5__table',
+        'time_grain_sqla': None,
+        'groupby': [],
+    }
+    print('Creating Screen Grid slice')
+    slc = Slice(
+        slice_name='Screen grid',
+        viz_type='deck_screengrid',
+        datasource_type='table',
+        datasource_id=tbl.id,
+        params=get_slice_json(slice_data),
+    )
+    merge_slice(slc)
+    slices.append(slc)
+
+    slice_data = {
+        'spatial': {
+            'type': 'latlong',
+            'lonCol': 'LON',
+            'latCol': 'LAT',
+        },
+        'filters': [],
+        'row_limit': 5000,
+        'mapbox_style': 'mapbox://styles/mapbox/streets-v9',
+        'granularity_sqla': None,
+        'size': 'count',
+        'viz_type': 'deck_hex',
+        'time_range': 'No filter',
+        'point_radius_unit': 'Pixels',
+        'point_radius': 'Auto',
+        'color_picker': {
+            'a': 1,
+            'r': 14,
+            'b': 0,
+            'g': 255,
+        },
+        'grid_size': 40,
+        'extruded': True,
+        'having': '',
+        'viewport': {
+            'latitude': 37.789795085160335,
+            'pitch': 54.08961642447763,
+            'zoom': 13.835465702403654,
+            'longitude': -122.40632230075536,
+            'bearing': -2.3984797349335167,
+        },
+        'where': '',
+        'point_radius_fixed': {'type': 'fix', 'value': 2000},
+        'datasource': '5__table',
+        'time_grain_sqla': None,
+        'groupby': [],
+    }
+    print('Creating Hex slice')
+    slc = Slice(
+        slice_name='Hexagons',
+        viz_type='deck_hex',
+        datasource_type='table',
+        datasource_id=tbl.id,
+        params=get_slice_json(slice_data),
+    )
+    merge_slice(slc)
+    slices.append(slc)
+
+    slice_data = {
+        'spatial': {
+            'type': 'latlong',
+            'lonCol': 'LON',
+            'latCol': 'LAT',
+        },
+        'filters': [],
+        'row_limit': 5000,
+        'mapbox_style': 'mapbox://styles/mapbox/satellite-streets-v9',
+        'granularity_sqla': None,
+        'size': 'count',
+        'viz_type': 'deck_grid',
+        'point_radius_unit': 'Pixels',
+        'point_radius': 'Auto',
+        'time_range': 'No filter',
+        'color_picker': {
+            'a': 1,
+            'r': 14,
+            'b': 0,
+            'g': 255,
+        },
+        'grid_size': 120,
+        'extruded': True,
+        'having': '',
+        'viewport': {
+            'longitude': -122.42066918995666,
+            'bearing': 155.80099696026355,
+            'zoom': 12.699690845482069,
+            'latitude': 37.7942314882596,
+            'pitch': 53.470800300695146,
+        },
+        'where': '',
+        'point_radius_fixed': {'type': 'fix', 'value': 2000},
+        'datasource': '5__table',
+        'time_grain_sqla': None,
+        'groupby': [],
+    }
+    print('Creating Grid slice')
+    slc = Slice(
+        slice_name='Grid',
+        viz_type='deck_grid',
+        datasource_type='table',
+        datasource_id=tbl.id,
+        params=get_slice_json(slice_data),
+    )
+    merge_slice(slc)
+    slices.append(slc)
+
+    polygon_tbl = db.session.query(TBL) \
+                    .filter_by(table_name='sf_population_polygons').first()
+    slice_data = {
+        'datasource': '11__table',
+        'viz_type': 'deck_polygon',
+        'slice_id': 41,
+        'granularity_sqla': None,
+        'time_grain_sqla': None,
+        'time_range': ' : ',
+        'line_column': 'contour',
+        'metric': None,
+        'line_type': 'json',
+        'mapbox_style': 'mapbox://styles/mapbox/light-v9',
+        'viewport': {
+            'longitude': -122.43388541747726,
+            'latitude': 37.752020331384834,
+            'zoom': 11.133995608594631,
+            'bearing': 37.89506450385642,
+            'pitch': 60,
+            'width': 667,
+            'height': 906,
+            'altitude': 1.5,
+            'maxZoom': 20,
+            'minZoom': 0,
+            'maxPitch': 60,
+            'minPitch': 0,
+            'maxLatitude': 85.05113,
+            'minLatitude': -85.05113,
+        },
+        'reverse_long_lat': False,
+        'fill_color_picker': {
+            'r': 3,
+            'g': 65,
+            'b': 73,
+            'a': 1,
+        },
+        'stroke_color_picker': {
+            'r': 0,
+            'g': 122,
+            'b': 135,
+            'a': 1,
+        },
+        'filled': True,
+        'stroked': False,
+        'extruded': True,
+        'point_radius_scale': 100,
+        'js_columns': [
+            'population',
+            'area',
+        ],
+        'js_datapoint_mutator':
+            '(d) => {\n    d.elevation = d.extraProps.population/d.extraProps.area/10\n \
+         d.fillColor = [d.extraProps.population/d.extraProps.area/60,140,0]\n \
+         return d;\n}',
+        'js_tooltip': '',
+        'js_onclick_href': '',
+        'where': '',
+        'having': '',
+        'filters': [],
+    }
+
+    print('Creating Polygon slice')
+    slc = Slice(
+        slice_name='Polygons',
+        viz_type='deck_polygon',
+        datasource_type='table',
+        datasource_id=polygon_tbl.id,
+        params=get_slice_json(slice_data),
+    )
+    merge_slice(slc)
+    slices.append(slc)
+
+    slice_data = {
+        'datasource': '10__table',
+        'viz_type': 'deck_arc',
+        'slice_id': 42,
+        'granularity_sqla': None,
+        'time_grain_sqla': None,
+        'time_range': ' : ',
+        'start_spatial': {
+            'type': 'latlong',
+            'latCol': 'LATITUDE',
+            'lonCol': 'LONGITUDE',
+        },
+        'end_spatial': {
+            'type': 'latlong',
+            'latCol': 'LATITUDE_DEST',
+            'lonCol': 'LONGITUDE_DEST',
+        },
+        'row_limit': 5000,
+        'mapbox_style': 'mapbox://styles/mapbox/light-v9',
+        'viewport': {
+            'altitude': 1.5,
+            'bearing': 8.546256357301871,
+            'height': 642,
+            'latitude': 44.596651438714254,
+            'longitude': -91.84340711201104,
+            'maxLatitude': 85.05113,
+            'maxPitch': 60,
+            'maxZoom': 20,
+            'minLatitude': -85.05113,
+            'minPitch': 0,
+            'minZoom': 0,
+            'pitch': 60,
+            'width': 997,
+            'zoom': 2.929837070560775,
+        },
+        'color_picker': {
+            'r': 0,
+            'g': 122,
+            'b': 135,
+            'a': 1,
+        },
+        'stroke_width': 1,
+        'where': '',
+        'having': '',
+        'filters': [],
+    }
+
+    print('Creating Arc slice')
+    slc = Slice(
+        slice_name='Arcs',
+        viz_type='deck_arc',
+        datasource_type='table',
+        datasource_id=db.session.query(TBL).filter_by(table_name='flights').first().id,
+        params=get_slice_json(slice_data),
+    )
+    merge_slice(slc)
+    slices.append(slc)
+
+    slice_data = {
+        'datasource': '12__table',
+        'slice_id': 43,
+        'viz_type': 'deck_path',
+        'time_grain_sqla': None,
+        'time_range': ' : ',
+        'line_column': 'path_json',
+        'line_type': 'json',
+        'row_limit': 5000,
+        'mapbox_style': 'mapbox://styles/mapbox/light-v9',
+        'viewport': {
+            'longitude': -122.18885402582598,
+            'latitude': 37.73671752604488,
+            'zoom': 9.51847667620428,
+            'bearing': 0,
+            'pitch': 0,
+            'width': 669,
+            'height': 1094,
+            'altitude': 1.5,
+            'maxZoom': 20,
+            'minZoom': 0,
+            'maxPitch': 60,
+            'minPitch': 0,
+            'maxLatitude': 85.05113,
+            'minLatitude': -85.05113,
+        },
+        'color_picker': {
+            'r': 0,
+            'g': 122,
+            'b': 135,
+            'a': 1,
+        },
+        'line_width': 150,
+        'reverse_long_lat': False,
+        'js_columns': [
+            'color',
+        ],
+        'js_datapoint_mutator': 'd => {\n    return {\n        ...d,\n        color: \
+            colors.hexToRGB(d.extraProps.color),\n    }\n}',
+        'js_tooltip': '',
+        'js_onclick_href': '',
+        'where': '',
+        'having': '',
+        'filters': [],
+    }
+
+    print('Creating Path slice')
+    slc = Slice(
+        slice_name='Path',
+        viz_type='deck_path',
+        datasource_type='table',
+        datasource_id=db.session.query(TBL).filter_by(table_name='bart_lines').first().id,
+        params=get_slice_json(slice_data),
+    )
+    merge_slice(slc)
+    slices.append(slc)
+    slug = 'deck'
+
+    print('Creating a dashboard')
+    title = 'deck.gl Demo'
+    dash = db.session.query(Dash).filter_by(slug=slug).first()
+
+    if not dash:
+        dash = Dash()
+    js = POSITION_JSON
+    pos = json.loads(js)
+    update_slice_ids(pos, slices)
+    dash.position_json = json.dumps(pos, indent=4)
+    dash.dashboard_title = title
+    dash.slug = slug
+    dash.slices = slices
+    db.session.merge(dash)
+    db.session.commit()
+
+
+if __name__ == '__main__':
+    load_deck_dash()
diff --git a/superset/data/energy.py b/superset/data/energy.py
new file mode 100644
index 000000000..bbb4fba49
--- /dev/null
+++ b/superset/data/energy.py
@@ -0,0 +1,116 @@
+"""Loads datasets, dashboards and slices in a new superset instance"""
+# pylint: disable=C,R,W
+import gzip
+import os
+import textwrap
+
+import pandas as pd
+from sqlalchemy import Float, String
+
+from superset import db
+from superset.utils import core as utils
+from .helpers import DATA_FOLDER, merge_slice, misc_dash_slices, Slice, TBL
+
+
+def load_energy():
+    """Loads an energy related dataset to use with sankey and graphs"""
+    tbl_name = 'energy_usage'
+    with gzip.open(os.path.join(DATA_FOLDER, 'energy.json.gz')) as f:
+        pdf = pd.read_json(f)
+    pdf.to_sql(
+        tbl_name,
+        db.engine,
+        if_exists='replace',
+        chunksize=500,
+        dtype={
+            'source': String(255),
+            'target': String(255),
+            'value': Float(),
+        },
+        index=False)
+
+    print('Creating table [wb_health_population] reference')
+    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
+    if not tbl:
+        tbl = TBL(table_name=tbl_name)
+    tbl.description = 'Energy consumption'
+    tbl.database = utils.get_or_create_main_db()
+    db.session.merge(tbl)
+    db.session.commit()
+    tbl.fetch_metadata()
+
+    slc = Slice(
+        slice_name='Energy Sankey',
+        viz_type='sankey',
+        datasource_type='table',
+        datasource_id=tbl.id,
+        params=textwrap.dedent("""\
+        {
+            "collapsed_fieldsets": "",
+            "groupby": [
+                "source",
+                "target"
+            ],
+            "having": "",
+            "metric": "sum__value",
+            "row_limit": "5000",
+            "slice_name": "Energy Sankey",
+            "viz_type": "sankey",
+            "where": ""
+        }
+        """),
+    )
+    misc_dash_slices.add(slc.slice_name)
+    merge_slice(slc)
+
+    slc = Slice(
+        slice_name='Energy Force Layout',
+        viz_type='directed_force',
+        datasource_type='table',
+        datasource_id=tbl.id,
+        params=textwrap.dedent("""\
+        {
+            "charge": "-500",
+            "collapsed_fieldsets": "",
+            "groupby": [
+                "source",
+                "target"
+            ],
+            "having": "",
+            "link_length": "200",
+            "metric": "sum__value",
+            "row_limit": "5000",
+            "slice_name": "Force",
+            "viz_type": "directed_force",
+            "where": ""
+        }
+        """),
+    )
+    misc_dash_slices.add(slc.slice_name)
+    merge_slice(slc)
+
+    slc = Slice(
+        slice_name='Heatmap',
+        viz_type='heatmap',
+        datasource_type='table',
+        datasource_id=tbl.id,
+        params=textwrap.dedent("""\
+        {
+            "all_columns_x": "source",
+            "all_columns_y": "target",
+            "canvas_image_rendering": "pixelated",
+            "collapsed_fieldsets": "",
+            "having": "",
+            "linear_color_scheme": "blue_white_yellow",
+            "metric": "sum__value",
+            "normalize_across": "heatmap",
+            "slice_name": "Heatmap",
+            "viz_type": "heatmap",
+            "where": "",
+            "xscale_interval": "1",
+            "yscale_interval": "1"
+        }
+        """),
+    )
+    misc_dash_slices.add(slc.slice_name)
+    merge_slice(slc)
diff --git a/superset/data/flights.py b/superset/data/flights.py
new file mode 100644
index 000000000..bfda80cf3
--- /dev/null
+++ b/superset/data/flights.py
@@ -0,0 +1,48 @@
+import gzip
+import os
+
+import pandas as pd
+from sqlalchemy import DateTime
+
+from superset import db
+from superset.utils import core as utils
+from .helpers import DATA_FOLDER, TBL
+
+
+def load_flights():
+    """Loading random time series data from a zip file in the repo"""
+    tbl_name = 'flights'
+    with gzip.open(os.path.join(DATA_FOLDER, 'fligth_data.csv.gz')) as f:
+        pdf = pd.read_csv(f, encoding='latin-1')
+
+    # Loading airports info to join and get lat/long
+    with gzip.open(os.path.join(DATA_FOLDER, 'airports.csv.gz')) as f:
+        airports = pd.read_csv(f, encoding='latin-1')
+    airports = airports.set_index('IATA_CODE')
+
+    pdf['ds'] = pdf.YEAR.map(str) + '-0' + pdf.MONTH.map(str) + '-0' + pdf.DAY.map(str)
+    pdf.ds = pd.to_datetime(pdf.ds)
+    del pdf['YEAR']
+    del pdf['MONTH']
+    del pdf['DAY']
+
+    pdf = pdf.join(airports, on='ORIGIN_AIRPORT', rsuffix='_ORIG')
+    pdf = pdf.join(airports, on='DESTINATION_AIRPORT', rsuffix='_DEST')
+    pdf.to_sql(
+        tbl_name,
+        db.engine,
+        if_exists='replace',
+        chunksize=500,
+        dtype={
+            'ds': DateTime,
+        },
+        index=False)
+    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
+    if not tbl:
+        tbl = TBL(table_name=tbl_name)
+    tbl.description = 'Random set of flights in the US'
+    tbl.database = utils.get_or_create_main_db()
+    db.session.merge(tbl)
+    db.session.commit()
+    tbl.fetch_metadata()
+    print('Done loading table!')
diff --git a/superset/data/helpers.py b/superset/data/helpers.py
new file mode 100644
index 000000000..d1ecccb95
--- /dev/null
+++ b/superset/data/helpers.py
@@ -0,0 +1,46 @@
+"""Loads datasets, dashboards and slices in a new superset instance"""
+# pylint: disable=C,R,W
+import json
+import os
+
+from superset import app, db
+from superset.connectors.connector_registry import ConnectorRegistry
+from superset.models import core as models
+
+# Shortcuts
+DB = models.Database
+Slice = models.Slice
+Dash = models.Dashboard
+
+TBL = ConnectorRegistry.sources['table']
+
+config = app.config
+
+DATA_FOLDER = os.path.join(config.get('BASE_DIR'), 'data')
+
+misc_dash_slices = set()  # slices assembled in a 'Misc Chart' dashboard
+
+
+def update_slice_ids(layout_dict, slices):
+    charts = [
+        component for component in layout_dict.values()
+        if isinstance(component, dict) and component['type'] == 'CHART'
+    ]
+    sorted_charts = sorted(charts, key=lambda k: k['meta']['chartId'])
+    for i, chart_component in enumerate(sorted_charts):
+        if i < len(slices):
+            chart_component['meta']['chartId'] = int(slices[i].id)
+
+
+def merge_slice(slc):
+    o = db.session.query(Slice).filter_by(slice_name=slc.slice_name).first()
+    if o:
+        db.session.delete(o)
+    db.session.add(slc)
+    db.session.commit()
+
+
+def get_slice_json(defaults, **kwargs):
+    d = defaults.copy()
+    d.update(kwargs)
+    return json.dumps(d, indent=4, sort_keys=True)
diff --git a/superset/data/long_lat.py b/superset/data/long_lat.py
new file mode 100644
index 000000000..c82452f7c
--- /dev/null
+++ b/superset/data/long_lat.py
@@ -0,0 +1,96 @@
+import datetime
+import gzip
+import os
+import random
+
+import geohash
+import pandas as pd
+from sqlalchemy import DateTime, Float, String
+
+from superset import db
+from superset.utils import core as utils
+from .helpers import (
+    DATA_FOLDER,
+    get_slice_json,
+    merge_slice,
+    misc_dash_slices,
+    Slice,
+    TBL,
+)
+
+
+def load_long_lat_data():
+    """Loading lat/long data from a csv file in the repo"""
+    with gzip.open(os.path.join(DATA_FOLDER, 'san_francisco.csv.gz')) as f:
+        pdf = pd.read_csv(f, encoding='utf-8')
+    start = datetime.datetime.now().replace(
+        hour=0, minute=0, second=0, microsecond=0)
+    pdf['datetime'] = [
+        start + datetime.timedelta(hours=i * 24 / (len(pdf) - 1))
+        for i in range(len(pdf))
+    ]
+    pdf['occupancy'] = [random.randint(1, 6) for _ in range(len(pdf))]
+    pdf['radius_miles'] = [random.uniform(1, 3) for _ in range(len(pdf))]
+    pdf['geohash'] = pdf[['LAT', 'LON']].apply(
+        lambda x: geohash.encode(*x), axis=1)
+    pdf['delimited'] = pdf['LAT'].map(str).str.cat(pdf['LON'].map(str), sep=',')
+    pdf.to_sql(  # pylint: disable=no-member
+        'long_lat',
+        db.engine,
+        if_exists='replace',
+        chunksize=500,
+        dtype={
+            'longitude': Float(),
+            'latitude': Float(),
+            'number': Float(),
+            'street': String(100),
+            'unit': String(10),
+            'city': String(50),
+            'district': String(50),
+            'region': String(50),
+            'postcode': Float(),
+            'id': String(100),
+            'datetime': DateTime(),
+            'occupancy': Float(),
+            'radius_miles': Float(),
+            'geohash': String(12),
+            'delimited': String(60),
+        },
+        index=False)
+    print('Done loading table!')
+    print('-' * 80)
+
+    print('Creating table reference')
+    obj = db.session.query(TBL).filter_by(table_name='long_lat').first()
+    if not obj:
+        obj = TBL(table_name='long_lat')
+    obj.main_dttm_col = 'datetime'
+    obj.database = utils.get_or_create_main_db()
+    db.session.merge(obj)
+    db.session.commit()
+    obj.fetch_metadata()
+    tbl = obj
+
+    slice_data = {
+        'granularity_sqla': 'day',
+        'since': '2014-01-01',
+        'until': 'now',
+        'where': '',
+        'viz_type': 'mapbox',
+        'all_columns_x': 'LON',
+        'all_columns_y': 'LAT',
+        'mapbox_style': 'mapbox://styles/mapbox/light-v9',
+        'all_columns': ['occupancy'],
+        'row_limit': 500000,
+    }
+
+    print('Creating a slice')
+    slc = Slice(
+        slice_name='Mapbox Long/Lat',
+        viz_type='mapbox',
+        datasource_type='table',
+        datasource_id=tbl.id,
+        params=get_slice_json(slice_data),
+    )
+    misc_dash_slices.add(slc.slice_name)
+    merge_slice(slc)
diff --git a/superset/data/misc_dashboard.py b/superset/data/misc_dashboard.py
new file mode 100644
index 000000000..3e29abe1b
--- /dev/null
+++ b/superset/data/misc_dashboard.py
@@ -0,0 +1,212 @@
+import json
+import textwrap
+
+
+from superset import db
+from .helpers import (
+    Dash,
+    misc_dash_slices,
+    Slice,
+    update_slice_ids,
+)
+
+DASH_SLUG = 'misc_charts'
+
+
+def load_misc_dashboard():
+    """Loading a dashboard featuring misc charts"""
+
+    print('Creating the dashboard')
+    db.session.expunge_all()
+    dash = db.session.query(Dash).filter_by(slug=DASH_SLUG).first()
+
+    if not dash:
+        dash = Dash()
+    js = textwrap.dedent("""\
+{
+    "CHART-BkeVbh8ANQ": {
+        "children": [],
+        "id": "CHART-BkeVbh8ANQ",
+        "meta": {
+            "chartId": 4004,
+            "height": 34,
+            "sliceName": "Multi Line",
+            "width": 8
+        },
+        "type": "CHART"
+    },
+    "CHART-H1HYNzEANX": {
+        "children": [],
+        "id": "CHART-H1HYNzEANX",
+        "meta": {
+            "chartId": 3940,
+            "height": 50,
+            "sliceName": "Energy Sankey",
+            "width": 6
+        },
+        "type": "CHART"
+    },
+    "CHART-HJOYVMV0E7": {
+        "children": [],
+        "id": "CHART-HJOYVMV0E7",
+        "meta": {
+            "chartId": 3969,
+            "height": 63,
+            "sliceName": "Mapbox Long/Lat",
+            "width": 6
+        },
+        "type": "CHART"
+    },
+    "CHART-S1WYNz4AVX": {
+        "children": [],
+        "id": "CHART-S1WYNz4AVX",
+        "meta": {
+            "chartId": 3989,
+            "height": 25,
+            "sliceName": "Parallel Coordinates",
+            "width": 4
+        },
+        "type": "CHART"
+    },
+    "CHART-r19KVMNCE7": {
+        "children": [],
+        "id": "CHART-r19KVMNCE7",
+        "meta": {
+            "chartId": 3978,
+            "height": 34,
+            "sliceName": "Calendar Heatmap multiformat 7",
+            "width": 4
+        },
+        "type": "CHART"
+    },
+    "CHART-rJ4K4GV04Q": {
+        "children": [],
+        "id": "CHART-rJ4K4GV04Q",
+        "meta": {
+            "chartId": 3941,
+            "height": 63,
+            "sliceName": "Energy Force Layout",
+            "width": 6
+        },
+        "type": "CHART"
+    },
+    "CHART-rkgF4G4A4X": {
+        "children": [],
+        "id": "CHART-rkgF4G4A4X",
+        "meta": {
+            "chartId": 3970,
+            "height": 25,
+            "sliceName": "Birth in France by department in 2016",
+            "width": 8
+        },
+        "type": "CHART"
+    },
+    "CHART-rywK4GVR4X": {
+        "children": [],
+        "id": "CHART-rywK4GVR4X",
+        "meta": {
+            "chartId": 3942,
+            "height": 50,
+            "sliceName": "Heatmap",
+            "width": 6
+        },
+        "type": "CHART"
+    },
+    "COLUMN-ByUFVf40EQ": {
+        "children": [
+            "CHART-rywK4GVR4X",
+            "CHART-HJOYVMV0E7"
+        ],
+        "id": "COLUMN-ByUFVf40EQ",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT",
+            "width": 6
+        },
+        "type": "COLUMN"
+    },
+    "COLUMN-rkmYVGN04Q": {
+        "children": [
+            "CHART-rJ4K4GV04Q",
+            "CHART-H1HYNzEANX"
+        ],
+        "id": "COLUMN-rkmYVGN04Q",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT",
+            "width": 6
+        },
+        "type": "COLUMN"
+    },
+    "GRID_ID": {
+        "children": [
+            "ROW-SytNzNA4X",
+            "ROW-S1MK4M4A4X",
+            "ROW-HkFFEzVRVm"
+        ],
+        "id": "GRID_ID",
+        "type": "GRID"
+    },
+    "HEADER_ID": {
+        "id": "HEADER_ID",
+        "meta": {
+            "text": "Misc Charts"
+        },
+        "type": "HEADER"
+    },
+    "ROOT_ID": {
+        "children": [
+            "GRID_ID"
+        ],
+        "id": "ROOT_ID",
+        "type": "ROOT"
+    },
+    "ROW-HkFFEzVRVm": {
+        "children": [
+            "CHART-r19KVMNCE7",
+            "CHART-BkeVbh8ANQ"
+        ],
+        "id": "ROW-HkFFEzVRVm",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW"
+    },
+    "ROW-S1MK4M4A4X": {
+        "children": [
+            "COLUMN-rkmYVGN04Q",
+            "COLUMN-ByUFVf40EQ"
+        ],
+        "id": "ROW-S1MK4M4A4X",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW"
+    },
+    "ROW-SytNzNA4X": {
+        "children": [
+            "CHART-rkgF4G4A4X",
+            "CHART-S1WYNz4AVX"
+        ],
+        "id": "ROW-SytNzNA4X",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW"
+    },
+    "DASHBOARD_VERSION_KEY": "v2"
+}
+    """)
+    pos = json.loads(js)
+    slices = (
+        db.session
+        .query(Slice)
+        .filter(Slice.slice_name.in_(misc_dash_slices))
+        .all()
+    )
+    slices = sorted(slices, key=lambda x: x.id)
+    update_slice_ids(pos, slices)
+    dash.dashboard_title = 'Misc Charts'
+    dash.position_json = json.dumps(pos, indent=4)
+    dash.slug = DASH_SLUG
+    dash.slices = slices
+    db.session.merge(dash)
+    db.session.commit()
diff --git a/superset/data/multi_line.py b/superset/data/multi_line.py
new file mode 100644
index 000000000..15468ec69
--- /dev/null
+++ b/superset/data/multi_line.py
@@ -0,0 +1,38 @@
+import json
+
+from superset import db
+from .birth_names import load_birth_names
+from .helpers import (
+    merge_slice,
+    misc_dash_slices,
+    Slice,
+)
+from .world_bank import load_world_bank_health_n_pop
+
+
+def load_multi_line():
+    load_world_bank_health_n_pop()
+    load_birth_names()
+    ids = [
+        row.id for row in
+        db.session.query(Slice).filter(
+            Slice.slice_name.in_(['Growth Rate', 'Trends']))
+    ]
+
+    slc = Slice(
+        datasource_type='table',  # not true, but needed
+        datasource_id=1,          # cannot be empty
+        slice_name='Multi Line',
+        viz_type='line_multi',
+        params=json.dumps({
+            'slice_name': 'Multi Line',
+            'viz_type': 'line_multi',
+            'line_charts': [ids[0]],
+            'line_charts_2': [ids[1]],
+            'since': '1960-01-01',
+            'prefix_metric_with_slice_name': True,
+        }),
+    )
+
+    misc_dash_slices.add(slc.slice_name)
+    merge_slice(slc)
diff --git a/superset/data/multiformat_time_series.py b/superset/data/multiformat_time_series.py
new file mode 100644
index 000000000..4e5cf6b46
--- /dev/null
+++ b/superset/data/multiformat_time_series.py
@@ -0,0 +1,92 @@
+import gzip
+import os
+
+import pandas as pd
+from sqlalchemy import BigInteger, Date, DateTime, String
+
+from superset import db
+from superset.utils import core as utils
+from .helpers import (
+    config,
+    DATA_FOLDER,
+    get_slice_json,
+    merge_slice,
+    misc_dash_slices,
+    Slice,
+    TBL,
+)
+
+
+def load_multiformat_time_series():
+    """Loading time series data from a zip file in the repo"""
+    with gzip.open(os.path.join(DATA_FOLDER, 'multiformat_time_series.json.gz')) as f:
+        pdf = pd.read_json(f)
+    pdf.ds = pd.to_datetime(pdf.ds, unit='s')
+    pdf.ds2 = pd.to_datetime(pdf.ds2, unit='s')
+    pdf.to_sql(
+        'multiformat_time_series',
+        db.engine,
+        if_exists='replace',
+        chunksize=500,
+        dtype={
+            'ds': Date,
+            'ds2': DateTime,
+            'epoch_s': BigInteger,
+            'epoch_ms': BigInteger,
+            'string0': String(100),
+            'string1': String(100),
+            'string2': String(100),
+            'string3': String(100),
+        },
+        index=False)
+    print('Done loading table!')
+    print('-' * 80)
+    print('Creating table [multiformat_time_series] reference')
+    obj = db.session.query(TBL).filter_by(table_name='multiformat_time_series').first()
+    if not obj:
+        obj = TBL(table_name='multiformat_time_series')
+    obj.main_dttm_col = 'ds'
+    obj.database = utils.get_or_create_main_db()
+    dttm_and_expr_dict = {
+        'ds': [None, None],
+        'ds2': [None, None],
+        'epoch_s': ['epoch_s', None],
+        'epoch_ms': ['epoch_ms', None],
+        'string2': ['%Y%m%d-%H%M%S', None],
+        'string1': ['%Y-%m-%d^%H:%M:%S', None],
+        'string0': ['%Y-%m-%d %H:%M:%S.%f', None],
+        'string3': ['%Y/%m/%d%H:%M:%S.%f', None],
+    }
+    for col in obj.columns:
+        dttm_and_expr = dttm_and_expr_dict[col.column_name]
+        col.python_date_format = dttm_and_expr[0]
+        col.dbatabase_expr = dttm_and_expr[1]
+        col.is_dttm = True
+    db.session.merge(obj)
+    db.session.commit()
+    obj.fetch_metadata()
+    tbl = obj
+
+    print('Creating Heatmap charts')
+    for i, col in enumerate(tbl.columns):
+        slice_data = {
+            'metrics': ['count'],
+            'granularity_sqla': col.column_name,
+            'row_limit': config.get('ROW_LIMIT'),
+            'since': '1 year ago',
+            'until': 'now',
+            'where': '',
+            'viz_type': 'cal_heatmap',
+            'domain_granularity': 'month',
+            'subdomain_granularity': 'day',
+        }
+
+        slc = Slice(
+            slice_name='Calendar Heatmap multiformat ' + str(i),
+            viz_type='cal_heatmap',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(slice_data),
+        )
+        merge_slice(slc)
+    misc_dash_slices.add(slc.slice_name)
diff --git a/superset/data/paris.py b/superset/data/paris.py
new file mode 100644
index 000000000..a8934d99a
--- /dev/null
+++ b/superset/data/paris.py
@@ -0,0 +1,40 @@
+import gzip
+import json
+import os
+
+import pandas as pd
+from sqlalchemy import String, Text
+
+from superset import db
+from superset.utils import core as utils
+from .helpers import DATA_FOLDER, TBL
+
+
+def load_paris_iris_geojson():
+    tbl_name = 'paris_iris_mapping'
+
+    with gzip.open(os.path.join(DATA_FOLDER, 'paris_iris.json.gz')) as f:
+        df = pd.read_json(f)
+        df['features'] = df.features.map(json.dumps)
+
+    df.to_sql(
+        tbl_name,
+        db.engine,
+        if_exists='replace',
+        chunksize=500,
+        dtype={
+            'color': String(255),
+            'name': String(255),
+            'features': Text,
+            'type': Text,
+        },
+        index=False)
+    print('Creating table {} reference'.format(tbl_name))
+    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
+    if not tbl:
+        tbl = TBL(table_name=tbl_name)
+    tbl.description = 'Map of Paris'
+    tbl.database = utils.get_or_create_main_db()
+    db.session.merge(tbl)
+    db.session.commit()
+    tbl.fetch_metadata()
diff --git a/superset/data/random_time_series.py b/superset/data/random_time_series.py
new file mode 100644
index 000000000..297490b36
--- /dev/null
+++ b/superset/data/random_time_series.py
@@ -0,0 +1,67 @@
+import gzip
+import os
+
+import pandas as pd
+from sqlalchemy import DateTime
+
+from superset import db
+from superset.utils import core as utils
+from .helpers import (
+    config,
+    DATA_FOLDER,
+    get_slice_json,
+    merge_slice,
+    Slice,
+    TBL,
+)
+
+
+def load_random_time_series_data():
+    """Loading random time series data from a zip file in the repo"""
+    with gzip.open(os.path.join(DATA_FOLDER, 'random_time_series.json.gz')) as f:
+        pdf = pd.read_json(f)
+    pdf.ds = pd.to_datetime(pdf.ds, unit='s')
+    pdf.to_sql(
+        'random_time_series',
+        db.engine,
+        if_exists='replace',
+        chunksize=500,
+        dtype={
+            'ds': DateTime,
+        },
+        index=False)
+    print('Done loading table!')
+    print('-' * 80)
+
+    print('Creating table [random_time_series] reference')
+    obj = db.session.query(TBL).filter_by(table_name='random_time_series').first()
+    if not obj:
+        obj = TBL(table_name='random_time_series')
+    obj.main_dttm_col = 'ds'
+    obj.database = utils.get_or_create_main_db()
+    db.session.merge(obj)
+    db.session.commit()
+    obj.fetch_metadata()
+    tbl = obj
+
+    slice_data = {
+        'granularity_sqla': 'day',
+        'row_limit': config.get('ROW_LIMIT'),
+        'since': '1 year ago',
+        'until': 'now',
+        'metric': 'count',
+        'where': '',
+        'viz_type': 'cal_heatmap',
+        'domain_granularity': 'month',
+        'subdomain_granularity': 'day',
+    }
+
+    print('Creating a slice')
+    slc = Slice(
+        slice_name='Calendar Heatmap',
+        viz_type='cal_heatmap',
+        datasource_type='table',
+        datasource_id=tbl.id,
+        params=get_slice_json(slice_data),
+    )
+    merge_slice(slc)
diff --git a/superset/data/sf_population_polygons.py b/superset/data/sf_population_polygons.py
new file mode 100644
index 000000000..abd6fef82
--- /dev/null
+++ b/superset/data/sf_population_polygons.py
@@ -0,0 +1,40 @@
+import gzip
+import json
+import os
+
+import pandas as pd
+from sqlalchemy import BigInteger, Text
+
+from superset import db
+from superset.utils import core as utils
+from .helpers import DATA_FOLDER, TBL
+
+
+def load_sf_population_polygons():
+    tbl_name = 'sf_population_polygons'
+
+    with gzip.open(os.path.join(DATA_FOLDER, 'sf_population.json.gz')) as f:
+        df = pd.read_json(f)
+        df['contour'] = df.contour.map(json.dumps)
+
+    df.to_sql(
+        tbl_name,
+        db.engine,
+        if_exists='replace',
+        chunksize=500,
+        dtype={
+            'zipcode': BigInteger,
+            'population': BigInteger,
+            'contour': Text,
+            'area': BigInteger,
+        },
+        index=False)
+    print('Creating table {} reference'.format(tbl_name))
+    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
+    if not tbl:
+        tbl = TBL(table_name=tbl_name)
+    tbl.description = 'Population density of San Francisco'
+    tbl.database = utils.get_or_create_main_db()
+    db.session.merge(tbl)
+    db.session.commit()
+    tbl.fetch_metadata()
diff --git a/superset/data/unicode_test_data.py b/superset/data/unicode_test_data.py
new file mode 100644
index 000000000..561b9e0d5
--- /dev/null
+++ b/superset/data/unicode_test_data.py
@@ -0,0 +1,139 @@
+import datetime
+import json
+import os
+import random
+
+import pandas as pd
+from sqlalchemy import Date, Float, String
+
+from superset import db
+from superset.utils import core as utils
+from .helpers import (
+    config,
+    Dash,
+    DATA_FOLDER,
+    get_slice_json,
+    merge_slice,
+    Slice,
+    TBL,
+    update_slice_ids,
+)
+
+
+def load_unicode_test_data():
+    """Loading unicode test dataset from a csv file in the repo"""
+    df = pd.read_csv(os.path.join(DATA_FOLDER, 'unicode_utf8_unixnl_test.csv'),
+                     encoding='utf-8')
+    # generate date/numeric data
+    df['dttm'] = datetime.datetime.now().date()
+    df['value'] = [random.randint(1, 100) for _ in range(len(df))]
+    df.to_sql(  # pylint: disable=no-member
+        'unicode_test',
+        db.engine,
+        if_exists='replace',
+        chunksize=500,
+        dtype={
+            'phrase': String(500),
+            'short_phrase': String(10),
+            'with_missing': String(100),
+            'dttm': Date(),
+            'value': Float(),
+        },
+        index=False)
+    print('Done loading table!')
+    print('-' * 80)
+
+    print('Creating table [unicode_test] reference')
+    obj = db.session.query(TBL).filter_by(table_name='unicode_test').first()
+    if not obj:
+        obj = TBL(table_name='unicode_test')
+    obj.main_dttm_col = 'dttm'
+    obj.database = utils.get_or_create_main_db()
+    db.session.merge(obj)
+    db.session.commit()
+    obj.fetch_metadata()
+    tbl = obj
+
+    slice_data = {
+        'granularity_sqla': 'dttm',
+        'groupby': [],
+        'metric': 'sum__value',
+        'row_limit': config.get('ROW_LIMIT'),
+        'since': '100 years ago',
+        'until': 'now',
+        'where': '',
+        'viz_type': 'word_cloud',
+        'size_from': '10',
+        'series': 'short_phrase',
+        'size_to': '70',
+        'rotation': 'square',
+        'limit': '100',
+    }
+
+    print('Creating a slice')
+    slc = Slice(
+        slice_name='Unicode Cloud',
+        viz_type='word_cloud',
+        datasource_type='table',
+        datasource_id=tbl.id,
+        params=get_slice_json(slice_data),
+    )
+    merge_slice(slc)
+
+    print('Creating a dashboard')
+    dash = (
+        db.session.query(Dash)
+        .filter_by(dashboard_title='Unicode Test')
+        .first()
+    )
+
+    if not dash:
+        dash = Dash()
+    js = """\
+{
+    "CHART-Hkx6154FEm": {
+        "children": [],
+        "id": "CHART-Hkx6154FEm",
+        "meta": {
+            "chartId": 2225,
+            "height": 30,
+            "sliceName": "slice 1",
+            "width": 4
+        },
+        "type": "CHART"
+    },
+    "GRID_ID": {
+        "children": [
+            "ROW-SyT19EFEQ"
+        ],
+        "id": "GRID_ID",
+        "type": "GRID"
+    },
+    "ROOT_ID": {
+        "children": [
+            "GRID_ID"
+        ],
+        "id": "ROOT_ID",
+        "type": "ROOT"
+    },
+    "ROW-SyT19EFEQ": {
+        "children": [
+            "CHART-Hkx6154FEm"
+        ],
+        "id": "ROW-SyT19EFEQ",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW"
+    },
+    "DASHBOARD_VERSION_KEY": "v2"
+}
+    """
+    dash.dashboard_title = 'Unicode Test'
+    pos = json.loads(js)
+    update_slice_ids(pos, [slc])
+    dash.position_json = json.dumps(pos, indent=4)
+    dash.slug = 'unicode-test'
+    dash.slices = [slc]
+    db.session.merge(dash)
+    db.session.commit()
diff --git a/superset/data/world_bank.py b/superset/data/world_bank.py
new file mode 100644
index 000000000..b75a07946
--- /dev/null
+++ b/superset/data/world_bank.py
@@ -0,0 +1,461 @@
+"""Loads datasets, dashboards and slices in a new superset instance"""
+# pylint: disable=C,R,W
+import gzip
+import json
+import os
+import textwrap
+
+import pandas as pd
+from sqlalchemy import DateTime, String
+
+from superset import db
+from superset.utils import core as utils
+from .helpers import (
+    config,
+    Dash,
+    DATA_FOLDER,
+    get_slice_json,
+    merge_slice,
+    misc_dash_slices,
+    Slice,
+    TBL,
+    update_slice_ids,
+)
+
+
+def load_world_bank_health_n_pop():
+    """Loads the world bank health dataset, slices and a dashboard"""
+    tbl_name = 'wb_health_population'
+    with gzip.open(os.path.join(DATA_FOLDER, 'countries.json.gz')) as f:
+        pdf = pd.read_json(f)
+    pdf.columns = [col.replace('.', '_') for col in pdf.columns]
+    pdf.year = pd.to_datetime(pdf.year)
+    pdf.to_sql(
+        tbl_name,
+        db.engine,
+        if_exists='replace',
+        chunksize=50,
+        dtype={
+            'year': DateTime(),
+            'country_code': String(3),
+            'country_name': String(255),
+            'region': String(255),
+        },
+        index=False)
+
+    print('Creating table [wb_health_population] reference')
+    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
+    if not tbl:
+        tbl = TBL(table_name=tbl_name)
+    tbl.description = utils.readfile(os.path.join(DATA_FOLDER, 'countries.md'))
+    tbl.main_dttm_col = 'year'
+    tbl.database = utils.get_or_create_main_db()
+    tbl.filter_select_enabled = True
+    db.session.merge(tbl)
+    db.session.commit()
+    tbl.fetch_metadata()
+
+    defaults = {
+        'compare_lag': '10',
+        'compare_suffix': 'o10Y',
+        'limit': '25',
+        'granularity_sqla': 'year',
+        'groupby': [],
+        'metric': 'sum__SP_POP_TOTL',
+        'metrics': ['sum__SP_POP_TOTL'],
+        'row_limit': config.get('ROW_LIMIT'),
+        'since': '2014-01-01',
+        'until': '2014-01-02',
+        'time_range': '2014-01-01 : 2014-01-02',
+        'where': '',
+        'markup_type': 'markdown',
+        'country_fieldtype': 'cca3',
+        'secondary_metric': 'sum__SP_POP_TOTL',
+        'entity': 'country_code',
+        'show_bubbles': True,
+    }
+
+    print('Creating slices')
+    slices = [
+        Slice(
+            slice_name='Region Filter',
+            viz_type='filter_box',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='filter_box',
+                date_filter=False,
+                groupby=['region', 'country_name'])),
+        Slice(
+            slice_name="World's Population",
+            viz_type='big_number',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                since='2000',
+                viz_type='big_number',
+                compare_lag='10',
+                metric='sum__SP_POP_TOTL',
+                compare_suffix='over 10Y')),
+        Slice(
+            slice_name='Most Populated Countries',
+            viz_type='table',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='table',
+                metrics=['sum__SP_POP_TOTL'],
+                groupby=['country_name'])),
+        Slice(
+            slice_name='Growth Rate',
+            viz_type='line',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='line',
+                since='1960-01-01',
+                metrics=['sum__SP_POP_TOTL'],
+                num_period_compare='10',
+                groupby=['country_name'])),
+        Slice(
+            slice_name='% Rural',
+            viz_type='world_map',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='world_map',
+                metric='sum__SP_RUR_TOTL_ZS',
+                num_period_compare='10')),
+        Slice(
+            slice_name='Life Expectancy VS Rural %',
+            viz_type='bubble',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='bubble',
+                since='2011-01-01',
+                until='2011-01-02',
+                series='region',
+                limit=0,
+                entity='country_name',
+                x='sum__SP_RUR_TOTL_ZS',
+                y='sum__SP_DYN_LE00_IN',
+                size='sum__SP_POP_TOTL',
+                max_bubble_size='50',
+                filters=[{
+                    'col': 'country_code',
+                    'val': [
+                        'TCA', 'MNP', 'DMA', 'MHL', 'MCO', 'SXM', 'CYM',
+                        'TUV', 'IMY', 'KNA', 'ASM', 'ADO', 'AMA', 'PLW',
+                    ],
+                    'op': 'not in'}],
+            )),
+        Slice(
+            slice_name='Rural Breakdown',
+            viz_type='sunburst',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                viz_type='sunburst',
+                groupby=['region', 'country_name'],
+                secondary_metric='sum__SP_RUR_TOTL',
+                since='2011-01-01',
+                until='2011-01-01')),
+        Slice(
+            slice_name="World's Pop Growth",
+            viz_type='area',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                since='1960-01-01',
+                until='now',
+                viz_type='area',
+                groupby=['region'])),
+        Slice(
+            slice_name='Box plot',
+            viz_type='box_plot',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                since='1960-01-01',
+                until='now',
+                whisker_options='Min/max (no outliers)',
+                viz_type='box_plot',
+                groupby=['region'])),
+        Slice(
+            slice_name='Treemap',
+            viz_type='treemap',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                since='1960-01-01',
+                until='now',
+                viz_type='treemap',
+                metrics=['sum__SP_POP_TOTL'],
+                groupby=['region', 'country_code'])),
+        Slice(
+            slice_name='Parallel Coordinates',
+            viz_type='para',
+            datasource_type='table',
+            datasource_id=tbl.id,
+            params=get_slice_json(
+                defaults,
+                since='2011-01-01',
+                until='2011-01-01',
+                viz_type='para',
+                limit=100,
+                metrics=[
+                    'sum__SP_POP_TOTL',
+                    'sum__SP_RUR_TOTL_ZS',
+                    'sum__SH_DYN_AIDS'],
+                secondary_metric='sum__SP_POP_TOTL',
+                series='country_name')),
+    ]
+    misc_dash_slices.add(slices[-1].slice_name)
+    for slc in slices:
+        merge_slice(slc)
+
+    print("Creating a World's Health Bank dashboard")
+    dash_name = "World's Bank Data"
+    slug = 'world_health'
+    dash = db.session.query(Dash).filter_by(slug=slug).first()
+
+    if not dash:
+        dash = Dash()
+    js = textwrap.dedent("""\
+{
+    "CHART-36bfc934": {
+        "children": [],
+        "id": "CHART-36bfc934",
+        "meta": {
+            "chartId": 40,
+            "height": 25,
+            "sliceName": "Region Filter",
+            "width": 2
+        },
+        "type": "CHART"
+    },
+    "CHART-37982887": {
+        "children": [],
+        "id": "CHART-37982887",
+        "meta": {
+            "chartId": 41,
+            "height": 25,
+            "sliceName": "World's Population",
+            "width": 2
+        },
+        "type": "CHART"
+    },
+    "CHART-17e0f8d8": {
+        "children": [],
+        "id": "CHART-17e0f8d8",
+        "meta": {
+            "chartId": 42,
+            "height": 92,
+            "sliceName": "Most Populated Countries",
+            "width": 3
+        },
+        "type": "CHART"
+    },
+    "CHART-2ee52f30": {
+        "children": [],
+        "id": "CHART-2ee52f30",
+        "meta": {
+            "chartId": 43,
+            "height": 38,
+            "sliceName": "Growth Rate",
+            "width": 6
+        },
+        "type": "CHART"
+    },
+    "CHART-2d5b6871": {
+        "children": [],
+        "id": "CHART-2d5b6871",
+        "meta": {
+            "chartId": 44,
+            "height": 52,
+            "sliceName": "% Rural",
+            "width": 7
+        },
+        "type": "CHART"
+    },
+    "CHART-0fd0d252": {
+        "children": [],
+        "id": "CHART-0fd0d252",
+        "meta": {
+            "chartId": 45,
+            "height": 50,
+            "sliceName": "Life Expectancy VS Rural %",
+            "width": 8
+        },
+        "type": "CHART"
+    },
+    "CHART-97f4cb48": {
+        "children": [],
+        "id": "CHART-97f4cb48",
+        "meta": {
+            "chartId": 46,
+            "height": 38,
+            "sliceName": "Rural Breakdown",
+            "width": 3
+        },
+        "type": "CHART"
+    },
+    "CHART-b5e05d6f": {
+        "children": [],
+        "id": "CHART-b5e05d6f",
+        "meta": {
+            "chartId": 47,
+            "height": 50,
+            "sliceName": "World's Pop Growth",
+            "width": 4
+        },
+        "type": "CHART"
+    },
+    "CHART-e76e9f5f": {
+        "children": [],
+        "id": "CHART-e76e9f5f",
+        "meta": {
+            "chartId": 48,
+            "height": 50,
+            "sliceName": "Box plot",
+            "width": 4
+        },
+        "type": "CHART"
+    },
+    "CHART-a4808bba": {
+        "children": [],
+        "id": "CHART-a4808bba",
+        "meta": {
+            "chartId": 49,
+            "height": 50,
+            "sliceName": "Treemap",
+            "width": 8
+        },
+        "type": "CHART"
+    },
+    "COLUMN-071bbbad": {
+        "children": [
+            "ROW-1e064e3c",
+            "ROW-afdefba9"
+        ],
+        "id": "COLUMN-071bbbad",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT",
+            "width": 9
+        },
+        "type": "COLUMN"
+    },
+    "COLUMN-fe3914b8": {
+        "children": [
+            "CHART-36bfc934",
+            "CHART-37982887"
+        ],
+        "id": "COLUMN-fe3914b8",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT",
+            "width": 2
+        },
+        "type": "COLUMN"
+    },
+    "GRID_ID": {
+        "children": [
+            "ROW-46632bc2",
+            "ROW-3fa26c5d",
+            "ROW-812b3f13"
+        ],
+        "id": "GRID_ID",
+        "type": "GRID"
+    },
+    "HEADER_ID": {
+        "id": "HEADER_ID",
+        "meta": {
+            "text": "World's Bank Data"
+        },
+        "type": "HEADER"
+    },
+    "ROOT_ID": {
+        "children": [
+            "GRID_ID"
+        ],
+        "id": "ROOT_ID",
+        "type": "ROOT"
+    },
+    "ROW-1e064e3c": {
+        "children": [
+            "COLUMN-fe3914b8",
+            "CHART-2d5b6871"
+        ],
+        "id": "ROW-1e064e3c",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW"
+    },
+    "ROW-3fa26c5d": {
+        "children": [
+            "CHART-b5e05d6f",
+            "CHART-0fd0d252"
+        ],
+        "id": "ROW-3fa26c5d",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW"
+    },
+    "ROW-46632bc2": {
+        "children": [
+            "COLUMN-071bbbad",
+            "CHART-17e0f8d8"
+        ],
+        "id": "ROW-46632bc2",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW"
+    },
+    "ROW-812b3f13": {
+        "children": [
+            "CHART-a4808bba",
+            "CHART-e76e9f5f"
+        ],
+        "id": "ROW-812b3f13",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW"
+    },
+    "ROW-afdefba9": {
+        "children": [
+            "CHART-2ee52f30",
+            "CHART-97f4cb48"
+        ],
+        "id": "ROW-afdefba9",
+        "meta": {
+            "background": "BACKGROUND_TRANSPARENT"
+        },
+        "type": "ROW"
+    },
+    "DASHBOARD_VERSION_KEY": "v2"
+}
+    """)
+    pos = json.loads(js)
+    update_slice_ids(pos, slices)
+
+    dash.dashboard_title = dash_name
+    dash.position_json = json.dumps(pos, indent=4)
+    dash.slug = slug
+
+    dash.slices = slices[:-1]
+    db.session.merge(dash)
+    db.session.commit()
diff --git a/superset/viz.py b/superset/viz.py
index bae48937b..d5b648148 100644
--- a/superset/viz.py
+++ b/superset/viz.py
@@ -784,7 +784,7 @@ class CalHeatmapViz(BaseViz):
         records = df.to_dict('records')
         for metric in self.metric_labels:
             data[metric] = {
-                str(obj[DTTM_ALIAS].value / 10**9): obj.get(metric)
+                str(obj[DTTM_ALIAS] / 10**9): obj.get(metric)
                 for obj in records
             }
 
diff --git a/tests/load_examples_test.py b/tests/load_examples_test.py
index d2b1005cd..273b9d573 100644
--- a/tests/load_examples_test.py
+++ b/tests/load_examples_test.py
@@ -24,7 +24,7 @@ class SupersetDataFrameTestCase(SupersetTestCase):
         data.load_country_map_data()
 
     def test_load_multiformat_time_series_data(self):
-        data.load_multiformat_time_series_data()
+        data.load_multiformat_time_series()
 
     def test_load_paris_iris_geojson(self):
         data.load_paris_iris_geojson()