Process & Analyze Azure Synapse Data in Databricks (AWS)

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Azure Synapse JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Azure Synapse.



Host the CData JDBC Driver for Azure Synapse in AWS and use Databricks to perform data engineering and data science on live Azure Synapse data.

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 Synapse data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Azure Synapse data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Azure Synapse data. When you issue complex SQL queries to Azure Synapse, the driver pushes supported SQL operations, like filters and aggregations, directly to Azure Synapse 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 Synapse data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Azure Synapse 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.azuresynapse.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Azure Synapse\lib).

Access Azure Synapse Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Azure Synapse 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 Synapse, and create a basic report.

Configure the Connection to Azure Synapse

Connect to Azure Synapse by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL.

Step 1: Connection Information

driver = "cdata.jdbc.azuresynapse.AzureSynapseDriver"
url = "jdbc:azuresynapse:User=myuser;Password=mypassword;Server=localhost;Database=Northwind;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.azuresynapse.jar

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

Connecting to Azure Synapse

In addition to providing authentication (see below), set the following properties to connect to a Azure Synapse database:

  • Server: The server running Azure. You can find this by logging into the Azure portal and navigating to Azure Synapse Analytics -> Select your database -> Overview -> Server name.
  • Database: The name of the database, as seen in the Azure portal on the Azure Synapse Analytics page.

Authenticating to Azure Synapse

Connect to Azure Synapse using the following properties:

  • User: The username provided for authentication with Azure.
  • Password: The password associated with the authenticating user.

Load Azure Synapse Data

Once you configure the connection, you can load Azure Synapse 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" , "Products") \
	.load ()

Display Azure Synapse Data

Check the loaded Azure Synapse data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

Analyze Azure Synapse 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 Synapse data for reporting, visualization, and analysis.

% sql

SELECT Id, ProductName FROM SAMPLE_VIEW ORDER BY ProductName DESC LIMIT 5

The data from Azure Synapse 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 Synapse and start working with your live Azure Synapse data in Databricks. Reach out to our Support Team if you have any questions.