Process & Analyze Azure Data Lake Storage Data in Databricks (AWS)

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Azure Data Lake Storage data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live Azure Data Lake Storage data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Azure Data Lake Storage data. When you issue complex SQL queries to Azure Data Lake Storage, the driver pushes supported SQL operations, like filters and aggregations, directly to Azure Data Lake Storage and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Azure Data Lake Storage data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Azure Data Lake Storage data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.adls.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

👁 Loading the JDBC JAR File into AWS

Access Azure Data Lake Storage Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Azure Data Lake Storage data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Azure Data Lake Storage, and create a basic report.

Configure the Connection to Azure Data Lake Storage

Connect to Azure Data Lake Storage by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.adls.ADLSDriver"
url = "jdbc:adls:RTK=5246...;Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;InitiateOAuth=GETANDREFRESH;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Azure Data Lake Storage JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.adls.jar

Fill in the connection properties and copy the connection string to the clipboard.

Authenticating to a Gen 1 DataLakeStore Account

Gen 1 uses OAuth 2.0 in Entra ID (formerly Azure AD) for authentication.

For this, an Active Directory web application is required. You can create one as follows:

1. Sign in to your Azure Account through the
2. Select "Entra ID" (formerly Azure AD).
3. Select "App registrations".
4. Select "New application registration".
5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

• Schema: Set this to ADLSGen1.
• Account: Set this to the name of the account.
• OAuthClientId: Set this to the application Id of the app you created.
• OAuthClientSecret: Set this to the key generated for the app you created.
• TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
• Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

Authenticating to a Gen 2 DataLakeStore Account

To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

• Schema: Set this to ADLSGen2.
• Account: Set this to the name of the account.
• FileSystem: Set this to the file system which will be used for this account.
• AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
• Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)

Load Azure Data Lake Storage Data

Once you configure the connection, you can load Azure Data Lake Storage data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Resources") \
	.load ()

Display Azure Data Lake Storage Data

Check the loaded Azure Data Lake Storage data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("FullPath"))

👁 Displaying Azure Data Lake Storage Data

Analyze Azure Data Lake Storage Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Azure Data Lake Storage data for reporting, visualization, and analysis.

% sql

SELECT FullPath, Permission FROM SAMPLE_VIEW ORDER BY Permission DESC LIMIT 5

👁 Displaying Azure Data Lake Storage Data

The data from Azure Data Lake Storage is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Azure Data Lake Storage and start working with your live Azure Data Lake Storage data in Databricks. Reach out to our Support Team if you have any questions.