Apache Flink is widely used for building real-time stream processing applications. On AWS, Amazon Managed Service for Apache Flink is the easiest option to develop a Flink app as it provides the underlying infrastructure. Updating a guide from AWS, this series of posts discuss how to develop and deploy a Flink (Pyflink) application via KDA where the data source and sink are Kafka topics. In part 1, the app will be developed locally targeting a Kafka cluster created by Docker. Furthermore, it will be executed in a virtual environment as well as in a local Flink cluster for improved monitoring.

Apache Flink is widely used for building real-time stream processing applications. On AWS, Amazon Managed Service for Apache Flink is the easiest option to develop a Flink app as it provides the underlying infrastructure. Re-implementing a solution from an AWS workshop, this series of posts discuss how to develop and deploy a fraud detection app using Kafka, Flink and DynamoDB. Part 1 covers local development using Docker while deployment via KDA will be discussed in part 2.

In the previous posts, we discussed how to implement client authentication by TLS (SSL or TLS/SSL) and SASL authentication. One of the key benefits of client authentication is achieving user access control. In this post, we will discuss how to configure Kafka authorization with Java and Python client examples while SASL is kept for client authentication.

In the previous post, we discussed TLS (SSL or TLS/SSL) authentication to improve security. It enforces two-way verification where a client certificate is verified by Kafka brokers. Client authentication can also be enabled by Simple Authentication and Security Layer (SASL), and we will discuss how to implement SASL authentication with Java and Python client examples in this post.

To improve security, we can extend TLS (SSL or TLS/SSL) encryption either by enforcing two-way verification where a client certificate is verified by Kafka brokers (SSL authentication). Or we can choose a separate authentication mechanism, which is typically Simple Authentication and Security Layer (SASL). In this post, we will discuss how to implement SSL authentication with Java and Python client examples while SASL authentication is covered in the next post.

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.

We can configure Kafka clients and other components to use TLS (SSL or TLS/SSL) encryption to secure communication. It is a one-way verification process where a server certificate is verified by a client via SSL Handshake. Moreover we can improve security by adding client authentication. In this post, we will discuss how to configure SSL encryption with Java and Python client examples while client authentication will be covered in later posts.

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.

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 Glue Schema Registry supports features to manage and enforce schemas on data streaming applications using convenient integrations with Apache Kafka and other AWS managed services. In order to utilise those features, we need to use the client library. In this post, I'll illustrate how to build the client library after introducing how it works to integrate the Glue Schema Registry with Kafka producer and consumer apps.