In Part 4, we developed Kafka producer and consumer applications using the kafka-python package without integrating schema registry. 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 applications by integrating AWS Glue Schema Registry.

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.

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 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.

Recently AWS Glue 3.0 was released but a docker image for this version is not published. In this post, I’ll illustrate how to create a development environment for AWS Glue 3.0 (and later versions) by building a custom docker image.

In this post, I'll demonstrate how to build development environments for AWS Glue 1.0 and 2.0 using the Docker image and the Visual Studio Code Remote - Containers extension.

In this post, it is demonstrated how AWS Lambda can be integrated with Apache Airflow using a custom operator inspired by the ECS Operator.