Amazon MSK - Amazon Timestream
Using Managed Service for Apache FlinkUsing Kafka Connect

Amazon MSK

Using Managed Service for Apache Flink to send Amazon MSK data to Timestream for LiveAnalytics

You can send data from Amazon MSK to Timestream by building a data connector similar to the sample Timestream data connector for Managed Service for Apache Flink. Refer to Amazon Managed Service for Apache Flink for more information.

Using Kafka Connect to send Amazon MSK data to Timestream for LiveAnalytics

You can use Kafka Connect to ingest your time series data from Amazon MSK directly into Timestream for LiveAnalytics.

We've created a sample Kafka Sink Connector for Timestream. We've also created a sample Apache jMeter test plan for publishing data to a Kafka topic, so that the data can flow from the topic through the Timestream Kafka Sink Connector, to an Timestream for LiveAnalytics table. All of these artifacts are available on GitHub.

Note

Java 11 is the recommended version for using the Timestream Kafka Sink Connector. If you have multiple Java versions, ensure that you export Java 11 to your JAVA_HOME environment variable.

Creating a sample application

To get started, follow the procedure below.

  1. In Timestream for LiveAnalytics, create a database with the name kafkastream.

    See the procedure Create a database for detailed instructions.

  2. In Timestream for LiveAnalytics, create a table with the name purchase_history.

    See the procedure Create a table for detailed instructions.

  3. Follow the instructions shared in the to create the following: , and .

    • An Amazon MSK cluster

    • An Amazon EC2 instance that is configured as a Kafka producer client machine

    • A Kafka topic

    See the prerequisites of the kafka_ingestor project for detailed instructions.

  4. Clone the Timestream Kafka Sink Connector repository.

    See Cloning a repository on GitHub for detailed instructions.

  5. Compile the plugin code.

    See Connector - Build from source on GitHub for detailed instructions.

  6. Upload the following files to an S3 bucket: following the instructions described in .

    • The jar file (kafka-connector-timestream->VERSION<-jar-with-dependencies.jar) from the /target directory

    • The sample json schema file, purchase_history.json.

    See Uploading objects in the Amazon S3 User Guide for detailed instructions.

  7. Create two VPC endpoints. These endpoints would be used by the MSK Connector to access the resources using AWS PrivateLink.

    • One to access the Amazon S3 bucket

    • One to access the Timestream for LiveAnalytics table.

    See VPC Endpoints for detailed instructions.

  8. Create a custom plugin with the uploaded jar file.

    See Plugins in the Amazon MSK Developer Guide for detailed instructions.

  9. Create a custom worker configuration with the JSON content described in Worker Configuration parameters. following the instructions described in

    See Creating a custom worker configuration in the Amazon MSK Developer Guide for detailed instructions.

  10. Create a service execution IAM role.

    See IAM Service Role for detailed instructions.

  11. Create an Amazon MSK connector with the custom plugin, custom worker configuration, and service execution IAM role created in the previous steps and with the Sample Connector Configuration.

    See Creating a connector in the Amazon MSK Developer Guide for detailed instructions.

    Make sure to update the values of the below configuration parameters with respective values. See Connector Configuration parameters for details.

    • aws.region

    • timestream.schema.s3.bucket.name

    • timestream.ingestion.endpoint

    The connector creation takes 5–10 minutes to complete. The pipeline is ready when its status changes to Running.

  12. Publish a continuous stream of messages for writing data to the Kafka topic created.

    See How to use it for detailed instructions.

  13. Run one or more queries to ensure that the data is being sent from Amazon MSK to MSK Connect to the Timestream for LiveAnalytics table.

    See the procedure Run a query for detailed instructions.

Additional resources

The blog, Real-time serverless data ingestion from your Kafka clusters into Timestream for LiveAnalytics using Kafka Connect explains setting up an end-to-end pipeline using the Timestream for LiveAnalytics Kafka Sink Connector, starting from a Kafka producer client machine that uses the Apache jMeter test plan to publish thousands of sample messages to a Kafka topic to verifying the ingested records in an Timestream for LiveAnalytics table.