diff --git a/work-with-data/dataprep/how-to-guides/datastore.ipynb b/work-with-data/dataprep/how-to-guides/datastore.ipynb
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--- a/work-with-data/dataprep/how-to-guides/datastore.ipynb
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-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/datastore.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Reading from and Writing to Datastores"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A datastore is a reference that points to an Azure storage service like a blob container for example. It belongs to a workspace and a workspace can have many datastores.\n",
-        "\n",
-        "A data path points to a path on the underlying Azure storage service the datastore references. For example, given a datastore named `blob` that points to an Azure blob container, a data path can point to `/test/data/titanic.csv` in the blob container."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read data from Datastore"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data Prep supports reading data from a `Datastore` or a `DataPath` or a `DataReference`. \n",
-        "\n",
-        "Passing in a datastore into all the `read_*` methods of Data Prep will result in reading everything in the underlying Azure storage service. To read a specific folder or file in the underlying storage, you have to pass in a data reference."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace, Datastore\n",
-        "from azureml.data.datapath import DataPath\n",
-        "\n",
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "First, get or create a workspace. Feel free to replace `subscription_id`, `resource_group`, and `workspace_name` with other values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "subscription_id = '35f16a99-532a-4a47-9e93-00305f6c40f2'\n",
-        "resource_group = 'DataStoreTest'\n",
-        "workspace_name = 'dataprep-centraleuap'\n",
-        "\n",
-        "workspace = Workspace(subscription_id=subscription_id, resource_group=resource_group, workspace_name=workspace_name)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "workspace.datastores"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can now read a crime data set from the datastore. If you are using your own workspace, the `crime0-10.csv` will not be there by default. You will have to upload the data to the datastore yourself."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore = Datastore(workspace=workspace, name='dataprep_blob')\n",
-        "dflow = dprep.read_csv(path=datastore.path('crime0-10.csv'))\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also read from an Azure SQL database. To do that, you will first get an Azure SQL database datastore instance and pass it to Data Prep for reading."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore = Datastore(workspace=workspace, name='test_sql')\n",
-        "dflow_sql = dprep.read_sql(data_source=datastore, query='SELECT * FROM team')\n",
-        "dflow_sql.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also read from a PostgreSQL database. To do that, you will first get a PostgreSQL database datastore instance and pass it to Data Prep for reading."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore = Datastore(workspace=workspace, name='postgre_test')\n",
-        "dflow_sql = dprep.read_postgresql(data_source=datastore, query='SELECT * FROM public.people')\n",
-        "dflow_sql.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Write data to Datastore"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also write a dataflow to a datastore. The code below will write the file you read in earlier to the folder in the datastore."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dest_datastore = Datastore(workspace, 'dataprep_blob_key')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow.write_to_csv(directory_path=dest_datastore.path('output/crime0-10')).run_local()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now you can read all the files in the `dataprep_adls` datastore which references an Azure Data Lake store."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "datastore = Datastore(workspace=workspace, name='dataprep_adls')\n",
-        "dflow_adls = dprep.read_csv(path=DataPath(datastore, path_on_datastore='/input/crime0-10.csv'))\n",
-        "dflow_adls.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now you can read all the files in the `dataprep_adlsgen2` datastore which references an ADLSGen2 Storage account."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# read a file from ADLSGen2\n",
-        "datastore = Datastore(workspace=workspace, name='adlsgen2')\n",
-        "dflow_adlsgen2 = dprep.read_csv(path=DataPath(datastore, path_on_datastore='/testfolder/peopletest.csv'))\n",
-        "dflow_adlsgen2.head(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# read all files from ADLSGen2 directory\n",
-        "datastore = Datastore(workspace=workspace, name='adlsgen2')\n",
-        "dflow_adlsgen2 = dprep.read_csv(path=DataPath(datastore, path_on_datastore='/testfolder/testdir'))\n",
-        "dflow_adlsgen2.head()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
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