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.
Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. It can be used to build real-time data pipeline on AWS effectively. In this post, I will introduce available Kafka connectors mainly for AWS services integration. Also, developing and deploying some of them will be covered in later posts.
Glue Schema Registry provides a centralized repository for managing and validating schemas for topic message data. Its features can be utilized by many AWS services when building data streaming applications. In this post, we will discuss how to integrate Python Kafka producer and consumer apps in AWS Lambda with the Glue Schema Registry.
Streaming ingestion from Kafka (MSK) into Redshift and Athena can be much simpler as they now support direct integration. In part 2, we discuss an end-to-end streaming ingestion solution using EventBridge, Lambda, MSK and Athena. We also use AWS SAM integrated with Terraform for developing the producer Lambda function locally.
Streaming ingestion from Kafka (MSK) into Redshift and Athena can be much simpler as they now support direct integration. In part 1, we discuss an end-to-end streaming ingestion solution using EventBridge, Lambda, MSK and Redshift. We also use AWS SAM integrated with Terraform for developing the producer Lambda function locally.
We will discuss how to configure the Kafka consumer to seek offsets by timestamp where topic partitions are dynamically assigned by subscription. Docker Compose is used for building a single node Kafka cluster and running multiple consumer instances.
We'll continue the discussion of a Change Data Capture (CDC) solution with a schema registry and its deployment to AWS. All major resources are deployed in private subnets and VPN is used to access them in order to improve developer experience. The Apicurio registry is used as the schema registry service and it is deployed as an ECS service. In order for the connectors to have access to the registry, the Confluent Avro Converter is packaged together with the connector sources. The post ends with illustrating how schema evolution is managed by the schema registry.
We'll discuss a Change Data Capture (CDC) architecture with a schema registry. As a starting point, a local development environment is set up using Docker Compose. The Debezium and Confluent S3 connectors are deployed with the Confluent Avro converter and the Apicurio registry is used as the schema registry service. A quick example is shown to illustrate how schema evolution can be managed by the schema registry.