Process & Analyze Kafka Data in Azure Databricks

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Apache Kafka JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Apache Kafka.



Host the CData JDBC Driver for Kafka in Azure and use Databricks to perform data engineering and data science on live Kafka 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 Kafka data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Kafka data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live Kafka data in Databricks, install the driver on your Azure 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.apachekafka.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Kafka\lib).

Connect to Kafka from Databricks

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

Configure the Connection to Kafka

Connect to Kafka by referencing the class for the JDBC Driver and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK 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.

driver = "cdata.jdbc.apachekafka.ApacheKafkaDriver"
url = "jdbc:apachekafka:RTK=5246...;User=admin;Password=pass;BootStrapServers=https://localhost:9091;Topic=MyTopic;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.apachekafka.jar

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

Set BootstrapServers and the Topic properties to specify the address of your Apache Kafka server, as well as the topic you would like to interact with.

Authorization Mechanisms

  • SASL Plain: The User and Password properties should be specified. AuthScheme should be set to 'Plain'.
  • SASL SSL: The User and Password properties should be specified. AuthScheme should be set to 'Scram'. UseSSL should be set to true.
  • SSL: The SSLCert and SSLCertPassword properties should be specified. UseSSL should be set to true.
  • Kerberos: The User and Password properties should be specified. AuthScheme should be set to 'Kerberos'.

You may be required to trust the server certificate. In such cases, specify the TrustStorePath and the TrustStorePassword if necessary.

Load Kafka Data

Once the connection is configured, you can load Kafka data as a dataframe using the CData JDBC Driver and the connection information.

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "SampleTable_1") \
	.load ()

Display Kafka Data

Check the loaded Kafka data by calling the display function.

display (remote_table.select ("Id"))

Analyze Kafka Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the Kafka data for analysis.

% sql

SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'

The data from Kafka 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 Kafka and start working with your live Kafka data in Apache NiFi. Reach out to our Support Team if you have any questions.