Stream SharePoint Data into Apache Kafka Topics



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

Prerequisites

Before connecting the CData JDBC Driver for streaming SharePoint 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 SharePoint data

  1. Download CData JDBC Driver for SharePoint 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 SharePoint mkdir SharePoint
    2. Move the downloaded driver file (.zip) into this new directory mv SharePointJDBCDriver.zip SharePoint/
    3. Unzip the CData SharePointJDBCDriver contents into this new directory unzip SharePointJDBCDriver.zip
  3. Open the SharePoint directory and navigate to the lib folder ls cd lib/
  4. Copy the contents of the lib folder of SharePoint into the lib folder of Kafka Connect JDBC. Check the Kafka Connect JDBC folder contents to confirm that the cdata.jdbc.sharepoint.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 SharePoint JDBC driver license using the given command, followed by your Name and Email ID java -jar cdata.jdbc.sharepoint.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 SharePoint 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_sharepoint_01", "config": { "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector", "connection.url": "jdbc:sharepoint:User=myuseraccount;Password=mypassword;Auth Scheme=NTLM;URL=http://sharepointserver/mysite;SharePointEdition=SharePointOnPremise;", "topic.prefix": "sharepoint-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 SharePoint data.

      Built-in Connection String Designer

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

      java -jar cdata.jdbc.sharepoint.jar

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

      Set the URL property to the base SharePoint site or to a sub-site. This allows you to query any lists and other SharePoint entities defined for the site or sub-site.

      The User and Password properties, under the Authentication section, must be set to valid SharePoint user credentials when using SharePoint On-Premise.

      If you are connecting to SharePoint Online, set the SharePointEdition to SHAREPOINTONLINE along with the User and Password connection string properties. For more details on connecting to SharePoint Online, see the "Getting Started" chapter of the help documentation

      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 "sharepoint-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 SharePoint 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 SharePoint and start streaming SharePoint data into Apache Kafka. Reach out to our Support Team if you have any questions.

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