Kafka and PyFlink replace Amazon Kinesis in a real time analytics app from an AWS workshop. The original and the new architecture are compared.

Deploy the Kafka, Flink and DynamoDB fraud detection app to Amazon Managed Service for Apache Flink, after developing it locally on Docker.

Deploy a PyFlink app that reads and writes Kafka topics on Amazon MSK to Amazon Managed Service for Apache Flink, the managed Flink runtime.

Connect a PyFlink app to an IAM authenticated MSK cluster, building a custom uber jar because Amazon Managed Service for Apache Flink takes only one jar.

Develop a PyFlink app against a Kafka cluster on Docker, running it both in a virtual environment and in a local Flink cluster for better monitoring.

Develop a fraud detection app locally on Docker with Kafka, Flink and DynamoDB, re-implementing a solution taken from an AWS workshop.

Control user access to Kafka resources with authorization rules, keeping SASL for client authentication, with Java and Python client examples.

Authenticate Kafka clients with SASL instead of client certificates, with Java and Python client examples of the SASL mechanisms.

Enforce two-way TLS verification so Kafka brokers check the client certificate, with Java and Python client examples of SSL authentication.

Deploy the Camel DynamoDB sink connector and its data generator source on Amazon MSK and MSK Connect, moving the local pipeline onto AWS.