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Stream IBM Cloud Data Engine Data into Apache Kafka Topics



Access and stream IBM Cloud Data Engine data in Apache Kafka using the CData JDBC Driver and the Kafka Connect JDBC connector.

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 IBM Cloud Data Engine, Kafka can work with live IBM Cloud Data Engine data. This article describes how to connect, access and stream IBM Cloud Data Engine data into Apache Kafka Topics and to start Confluent Control Center to help users secure, manage, and monitor the IBM Cloud Data Engine 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 IBM Cloud Data Engine data. When you issue complex SQL queries to IBM Cloud Data Engine, the driver pushes supported SQL operations, like filters and aggregations, directly to IBM Cloud Data Engine 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 IBM Cloud Data Engine data using native data types.

Prerequisites

Before connecting the CData JDBC Driver for streaming IBM Cloud Data Engine 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 IBM Cloud Data Engine data

  1. Download CData JDBC Driver for IBM Cloud Data Engine 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 IBM Cloud Data Engine mkdir IBMCloudDataEngine
    2. Move the downloaded driver file (.zip) into this new directory mv IBMCloudDataEngineJDBCDriver.zip IBMCloudDataEngine/
    3. Unzip the CData IBMCloudDataEngineJDBCDriver contents into this new directory unzip IBMCloudDataEngineJDBCDriver.zip
  3. Open the IBM Cloud Data Engine directory and navigate to the lib folder ls cd lib/
  4. Copy the contents of the lib folder of IBM Cloud Data Engine into the lib folder of Kafka Connect JDBC. Check the Kafka Connect JDBC folder contents to confirm that the cdata.jdbc.ibmclouddataengine.jar file is successfully copied into the lib folder cp * ../../confluent-7.5.0/share/confluent-hub-components/confluentinc-kafka-connect-jdbc/lib/ cd ../../confluent-7.5.0/share/confluent-hub-components/confluentinc-kafka-connect-jdbc/lib/
  5. Install the CData IBM Cloud Data Engine JDBC driver license using the given command, followed by your Name and Email ID java -jar cdata.jdbc.ibmclouddataengine.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 IBM Cloud Data Engine 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_ibmclouddataengine_01", "config": { "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector", "connection.url": "jdbc:ibmclouddataengine:Api Key=MyAPIKey;Instance CRN=myInstanceCRN;Region=myRegion;Schema=mySchema;OAuth Client Id=myOAuthClientId;OAuth Client Secret=myOAuthClientSecret;; InitiateOAuth=GETANDREFRESH", "topic.prefix": "ibmclouddataengine-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 IBM Cloud Data Engine data.

      Built-in Connection String Designer

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

      java -jar cdata.jdbc.ibmclouddataengine.jar

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

      IBM Cloud Data Engine uses the OAuth and HMAC authentication standards. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

      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 "ibmclouddataengine-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 IBM Cloud Data Engine 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.

Connect with Confluent Control Center

Get Started Today

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