How to work with Azure Synapse Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Azure Synapse, Spark can work with live Azure Synapse data. This article describes how to connect to and query Azure Synapse data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Azure Synapse data due to optimized data processing built into the driver. 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 (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Azure Synapse data using native data types.

Install the CData JDBC Driver for Azure Synapse

Download the CData JDBC Driver for Azure Synapse installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Azure Synapse Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Azure Synapse JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Azure Synapse/lib/cdata.jdbc.azuresynapse.jar
2. With the shell running, you can connect to Azure Synapse with a JDBC URL and use the SQL Context load() function to read a table.

   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.

   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.

   Configure the connection to Azure Synapse, using the connection string generated above.

   scala> val azuresynapse_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:azuresynapse:User=myuser;Password=mypassword;Server=localhost;Database=Northwind;").option("dbtable","Products").option("driver","cdata.jdbc.azuresynapse.AzureSynapseDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Azure Synapse data as a temporary table:

   scala> azuresynapse_df.registerTable("products")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> azuresynapse_df.sqlContext.sql("SELECT Id, ProductName FROM Products WHERE ProductName = Konbu").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

Using the CData JDBC Driver for Azure Synapse in Apache Spark, you are able to perform fast and complex analytics on Azure Synapse data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.