This series updates a real time analytics app based on Amazon Kinesis from an AWS workshop. Data is ingested from multiple sources into a Kafka cluster instead and Flink (Pyflink) apps are used extensively for data ingesting and processing. As an introduction, this post compares the original architecture with the new architecture, and the app will be implemented in subsequent posts.

As part of investigating how to utilize Kafka Connect effectively for AWS services integration, I demonstrated how to develop the Camel DynamoDB sink connector using Docker in Part 2. Fake order data was generated using the MSK Data Generator source connector, and the sink connector was configured to consume the topic messages to ingest them into a DynamoDB table. In this post, I will illustrate how to deploy the data ingestion applications using Amazon MSK and MSK Connect.

The suite of Apache Camel Kafka connectors and the Kinesis Kafka connector from the AWS Labs can be effective for building data ingestion pipelines that integrate AWS services. In this post, I will illustrate how to develop the Camel DynamoDB sink connector using Docker. Fake order data will be generated using the MSK Data Generator source connector, and the sink connector will be configured to consume the topic messages to ingest them into a DynamoDB table.