In Part 3, we developed a data ingestion pipeline using Kafka Connect source and sink connectors without enabling schemas. Later we discussed the benefits of schema registry when developing Kafka applications in Part 5. In this post, I'll demonstrate how to enhance the existing data ingestion pipeline by integrating AWS Glue Schema Registry.

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 includes the Producer/Consumer APIs that allow client applications to send/read streams of data to/from topics in a Kafka cluster. While the main Kafka project maintains only the Java clients, there are several open source projects that provide the Kafka client APIs in Python. In this post, I'll demonstrate how to develop producer/consumer applications using the kafka-python package.

Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. In this post, I will illustrate how to set up a data ingestion pipeline using Kafka connectors. Fake customer and order data will be ingested into the corresponding topics using the MSK Data Generator source connector. The topic messages will then be saved into a S3 bucket using the Confluent S3 sink connector.

A Kafka management app can be a good companion for development, which helps monitor and manage resources on an easy-to-use user interface. An app can be more useful if it supports features that are desirable for Kafka development on AWS. Those features cover IAM access control and integration with MSK Connect and Glue Schema Registry. In this post, I'll introduce several management apps that meet those requirements.

Apache Kafka is one of the key technologies for modern data streaming architectures on AWS. Developing and testing Kafka-related applications can be easier using Docker and Docker Compose. In this series of posts, I will demonstrate reference implementations of those applications in Dockerized environments.

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 discuss limitations of the Lambda invoke function operator of Apache Airflow and create a custom Lambda operator. The custom operator extends the existing one and it reports the invocation result of a function correctly and records the exact error message from failure.

We'll discuss how to create a Spark local dev environment for EMR using Docker and/or VSCode. A range of Spark development examples are demonstrated and Glue Catalog integration is illustrated as well.

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.