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

Prerequisites

Before connecting the CData JDBC Driver for streaming Placid 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 Placid data

1. Download CData API Driver for JDBC 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 Placid

      		mkdir API
      		

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

      		mv APIJDBCDriver.zip API/
      		

   3. Unzip the CData APIJDBCDriver contents into this new directory

      		unzip APIJDBCDriver.zip
      		

3. Open the Placid directory and navigate to the lib folder

   ls
   cd lib/
   

4. Copy the contents of the lib folder of the CData API Driver for JDBC into the lib folder of Kafka Connect JDBC. Check the Kafka Connect JDBC folder contents to confirm that the cdata.jdbc.api.jar file is successfully copied into the lib folder

   cp -r /path/to/CData API Driver for JDBC/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 Placid JDBC driver license using the given command, followed by your Name and Email ID

   	java -jar cdata.jdbc.api.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 Placid 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_api_01", 
   		"config": { 
   			"connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector", 
   			"connection.url": "jdbc:api:Profile=C:\profiles\Placid.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_project_api_token';",
   		"topic.prefix": "api-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 Placid data.

     Built-in Connection String Designer

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

     		java -jar cdata.jdbc.api.jar
     		

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

     Placid uses API Key authentication to control access to the API. API tokens are project-specific and can be obtained from your project settings on placid.app.

     Using API Key Authentication

     To obtain your API key, log in to placid.app, navigate to your project, open the project settings, and generate an API token from the API section. Note that each API token is scoped to a specific project.

     After setting the following connection properties, you are ready to connect:

     • AuthScheme: Set this to APIKey.
     • APIKey: Set this to your Placid project API token.

     Example connection string:

     Profile=C:\profiles\Placid.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_project_api_token';
     

     👁 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 "api-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 Placid 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 API Driver for JDBC and start streaming Placid data into Apache Kafka. Reach out to our Support Team if you have any questions.