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Tutorial: Extract, transform, and load data using Azure Databricks. 13 minutes to read. Contributors. In this article In this tutorial, you perform an ETL (extract, transform, and load data) operation using Azure Databricks. You extract data from Azure Data Lake Store into Azure Databricks, run transformations on the data in Azure Databricks, and then load the transformed data into Azure SQL Data Warehouse. The steps in this tutorial use the SQL Data Warehouse connector for Azure Databricks to transfer data to Azure Databricks.
This connector, in turn, uses Azure Blob Storage as temporary storage for the data being transferred between an Azure Databricks cluster and Azure SQL Data Warehouse. The following illustration shows the application flow: This tutorial covers the following tasks. Create an Azure Databricks workspace. Create a Spark cluster in Azure Databricks. Create an Azure Data Lake Store account. Upload data to Azure Data Lake Store. Create a notebook in Azure Databricks.
Extract data from Data Lake Store. Transform data in Azure Databricks. Load data into Azure SQL Data Warehouse If you don't have an Azure subscription, before you begin. Prerequisites Before you start with this tutorial, make sure to meet the following requirements:.
Create an Azure SQL Data Warehouse, create a server-level firewall rule, and connect to the server as a server admin. Follow the instructions at. Create a database master key for the Azure SQL Data Warehouse. Follow the instructions at. Create an Azure Blob storage account, and a container within it. Also, retrieve the access key to access the storage account.
Follow the instructions at. Log in to the Azure portal Log in to the. Create an Azure Databricks workspace In this section, you create an Azure Databricks workspace using the Azure portal. In the Azure portal, select Create a resource Data + Analytics Azure Databricks. Under Azure Databricks Service, provide the values to create a Databricks workspace. Provide the following values: Property Description Workspace name Provide a name for your Databricks workspace Subscription From the drop-down, select your Azure subscription.
Resource group Specify whether you want to create a new resource group or use an existing one. A resource group is a container that holds related resources for an Azure solution.
For more information, see. Location Select East US 2.
For other available regions, see. Pricing Tier Choose between Standard or Premium. For more information on these tiers, see. Select Pin to dashboard and then select Create. The account creation takes a few minutes. During account creation, the portal displays the Submitting deployment for Azure Databricks tile on the right side. You may need to scroll right on your dashboard to see the tile.
There is also a progress bar displayed near the top of the screen. You can watch either area for progress. Create a Spark cluster in Databricks.
In the Azure portal, go to the Databricks workspace that you created, and then select Launch Workspace. You are redirected to the Azure Databricks portal.
From the portal, select Cluster. In the New cluster page, provide the values to create a cluster.
Accept all other defaults other than the following values:. Enter a name for the cluster. For this article, create a cluster with 4.0 runtime. Make sure you select the Terminate after minutes of inactivity checkbox. Provide a duration (in minutes) to terminate the cluster, if the cluster is not being used.
Select Create cluster. Once the cluster is running, you can attach notebooks to the cluster and run Spark jobs.
Create an Azure Data Lake Store account In this section, you create an Azure Data Lake Store account and associate an Azure Active Directory service principal with it. Later in this tutorial, you use this service principal in Azure Databricks to access Azure Data Lake Store. From the, select Create a resource Storage Data Lake Store. In the New Data Lake Store blade, provide the values as shown in the following screenshot: Provide the following values: Property Description Name Enter a unique name for the Data Lake Store account.
Subscription From the drop-down, select your Azure subscription. Resource group For this tutorial, select the same resource group you used while creating the Azure Databricks workspace. Location Select East US 2. Pricing package Select Pay-as-you-go. Encryption Settings Keep the default settings. Select Pin to dashboard and then select Create.
You now create an Azure Active Directory service principal and associate with the Data Lake Store account you created. Create an Azure Active Directory service principal. From the, select All services, and then search for Azure Active Directory. Select App registrations. Select New application registration. Provide a name and URL for the application. Select Web app / API for the type of application you want to create.
Provide a sign-on URL, and then select Create. To access the Data Lake Store account from Azure Databricks, you must have the following values for the Azure Active Directory service principal you created:.
Application ID. Authentication key. Tenant ID In the following sections, you retrieve these values for the Azure Active Directory service principal you created earlier.
Get application ID and authentication key for the service principal When programmatically logging in, you need the ID for your application and an authentication key. To get those values, use the following steps:. From App registrations in Azure Active Directory, select your application. Copy the Application ID and store it in your application code.
Some refer to this value as the client ID. To generate an authentication key, select Settings. To generate an authentication key, select Keys. Provide a description of the key, and a duration for the key.
When done, select Save. After saving the key, the value of the key is displayed. Copy this value because you are not able to retrieve the key later. You provide the key value with the application ID to log in as the application.
Store the key value where your application can retrieve it. Get tenant ID When programmatically logging in, you need to pass the tenant ID with your authentication request. Select Azure Active Directory. To get the tenant ID, select Properties for your Azure AD tenant.
Copy the Directory ID. This value is your tenant ID. Upload data to Data Lake Store In this section, you upload a sample data file to Data Lake Store. You use this file later in Azure Databricks to run some transformations. The sample data ( smallradiojson.json) that you use in this tutorial is available in this. From the, select the Data Lake Store account you created. From the Overview tab, click Data Explorer.
Within the Data Explorer, click Upload. In Upload files, browse to the location of your sample data file, and then select Add selected files. In this tutorial, you uploaded the data file to the root of the Data Lake Store. So, the file is now available at adl://.azuredatalakestore.net/smallradiojson.json. Associate service principal with Azure Data Lake Store In this section, you associate the data in Azure Data Lake Store account with the Azure Active Directory service principal you created. This ensures that you can access the Data Lake Store account from Azure Databricks.
For the scenario in this article, you read the data in Data Lake Store to populate a table in SQL Data Warehouse. According to, to have read access on a file in Data Lake Store, you must have:. Execute permissions on all the folders in the folder structure leading up to the file.
Read permissions on the file itself. Perform the following steps to grant these permissions. From the, select the Data Lake Store account you created, and then select Data Explorer. In this scenario, because the sample data file is at the root of the folder structure, you only need to assign Execute permissions at the folder root. To do so, from the root of data explorer, select Access. Under Access, select Add. Under Assign permissions, click Select user or group and search for the Azure Active Directory service principal you created earlier.
Select the AAD service principal you want to assign and click Select. Under Assign permissions, click Select permissions Execute. Keep the other default values and select OK under Select permissions and then under Assign permissions. Go back to the Data Explorer and now click the file on which you want to assign the read permission.
Under File Preview, select Access. Under Access, select Add. Under Assign permissions, click Select user or group and search for the Azure Active Directory service principal you created earlier. Select the AAD service principal you want to assign and click Select. Under Assign permissions, click Select permissions Read.
Select OK under Select permissions and then under Assign permissions. The service principal now has sufficient permissions to read the sample data file from Azure Data Lake Store. Extract data from Data Lake Store In this section, you create a notebook in Azure Databricks workspace and then run code snippets to extract data from Data Lake Store into Azure Databricks.
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In the, go to the Azure Databricks workspace you created, and then select Launch Workspace. In the left pane, select Workspace.
From the Workspace drop-down, select Create Notebook. In the Create Notebook dialog box, enter a name for the notebook.
Select Scala as the language, and then select the Spark cluster that you created earlier. Select Create. Add the following snippet in an empty code cell and replace the placeholder values with the values you saved earlier for the Azure Active Directory service principal. Spark.conf.set('dfs.adls.oauth2.access.token.provider.type', 'ClientCredential') spark.conf.set('dfs.adls.oauth2.client.id', ') spark.conf.set('dfs.adls.oauth2.credential', ') spark.conf.set('dfs.adls.oauth2.refresh.url', 'Press SHIFT + ENTER to run the code cell. You can now load the sample json file in Data Lake Store as a dataframe in Azure Databricks. Past the following snippet in a new code cell, replace the placeholder value, and then press SHIFT + ENTER.
Val df = spark.read.json('adl://.azuredatalakestore.net/smallradiojson.json'). Run the following code snippet to see the contents of the data frame.