Stream Azure Data Lake Storage Data into Apache Kafka Topics

Apache Kafka is an open-source stream processing platform that is primarily used for building real-time data pipelines and event-driven applications. When paired with the CData JDBC Driver for Azure Data Lake Storage, Kafka can work with live Azure Data Lake Storage data. This article describes how to connect, access and stream Azure Data Lake Storage data into Apache Kafka Topics and to start Confluent Control Center to help users secure, manage, and monitor the Azure Data Lake Storage data received using Kafka infrastructure in the Confluent Platform.

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.

Prerequisites

Before connecting the CData JDBC Driver for streaming Azure Data Lake Storage data in Apache Kafka Topics, install and configure the following in the client Linux-based system.

1. Confluent Platform for Apache Kafka
2. Confluent Hub CLI Installation
3. Self-Managed Kafka JDBC Source Connector for Confluent Platform

Define a New JDBC Connection to Azure Data Lake Storage data

1. Download CData JDBC Driver for Azure Data Lake Storage on a Linux-based system
2. Follow the given instructions to create a new directory extract all the driver contents into it:
   1. Create a new directory named Azure Data Lake Storage

      		mkdir ADLS
      		

   2. Move the downloaded driver file (.zip) into this new directory

      		mv ADLSJDBCDriver.zip ADLS/
      		

   3. Unzip the CData ADLSJDBCDriver contents into this new directory

      		unzip ADLSJDBCDriver.zip
      		

3. Open the Azure Data Lake Storage directory and navigate to the lib folder

   ls
   cd lib/
   

4. Copy the contents of the lib folder of the CData JDBC Driver for Azure Data Lake Storage into the lib folder of Kafka Connect JDBC. Check the Kafka Connect JDBC folder contents to confirm that the cdata.jdbc.adls.jar file is successfully copied into the lib folder

   cp -r /path/to/CData JDBC Driver for Azure Data Lake Storage/lib/* /usr/share/confluent-hub-components/confluentinc-kafka-connect-jdbc/lib/
   cd /usr/share/confluent-hub-components/confluentinc-kafka-connect-jdbc/lib/
   

5. Install the CData Azure Data Lake Storage JDBC driver license using the given command, followed by your Name and Email ID

   	java -jar cdata.jdbc.adls.jar -l
   	

6. Enter the product key or "TRIAL" (In the scenarios of license expiry, please contact our CData Support team)
7. Start the Confluent local services using the command:

   	confluent local services start
   	

   This starts all the Confluent Services like Zookeeper, Kafka, Schema Registry, Kafka REST, Kafka CONNECT, ksqlDB and Control Center. You are now ready to use the CData JDBC driver for Azure Data Lake Storage to stream messages using Kafka Connect Driver into Kafka Topics on ksqlDB.

   👁 Start the Confluent local services
   
8. Create the Kafka topics manually using a POST HTTP API Request:

    curl --location 'server_address:8083/connectors' 
   	--header 'Content-Type: application/json'
   	--data '{ 
   		"name": "jdbc_source_cdata_adls_01", 
   		"config": { 
   			"connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector", 
   			"connection.url": "jdbc:adls:Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;InitiateOAuth=GETANDREFRESH;",
   		"topic.prefix": "adls-01-", 
   		"mode": "bulk" 
   		} 
   	}'
   

   Let us understand the fields used in the HTTP POST body (shown above):

   • connector.class: Specifies the Java class of the Kafka Connect connector to be used.
   • connection.url: The JDBC connection URL to connect with Azure Data Lake Storage data.

     Built-in Connection String Designer

     For assistance in constructing the JDBC URL, use the connection string designer built into the CData JDBC Driver for Azure Data Lake Storage. 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.)
     
   • topic.prefix: A prefix that will be added to the Kafka topics created by the connector. It's set to "adls-01-".
   • mode: Specifies the mode in which the connector operates. In this case, it's set to "bulk", which suggests that the connector is configured to perform bulk data transfer.

   This request adds all the tables/contents from Azure Data Lake Storage as Kafka Topics.

   Note: The IP Address (server) to POST the request (shown above) is the Linux Network IP Address.

9. Run ksqlDB and list the topics. Use the commands:

   ksql
   list topics;
   

   👁 List the Kafka Topics (BigCommerce is shown)
   
10. To view the data inside the topics, type the SQL Statement:

    PRINT topic FROM BEGINNING;
    

Connecting with the Confluent Control Center

To access the Confluent Control Center user interface, ensure to run the "confluent local services" as described in the above section and type http://<server address>:9021/clusters/ on your local browser.

Get Started Today

Download a free, 30-day trial of the CData JDBC Driver for Azure Data Lake Storage and start streaming Azure Data Lake Storage data into Apache Kafka. Reach out to our Support Team if you have any questions.