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

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

Schema registry support added to kafka-python producer and consumer apps, which serialise and deserialise through AWS Glue Schema Registry.

Schema registry support added to a Kafka Connect ingestion pipeline, so the source and sink connectors serialise through AWS Glue Schema Registry.

Build the Glue Schema Registry client library and use it to manage and enforce schemas in Kafka producer and consumer applications.

Develop the Camel DynamoDB sink connector on Docker, consuming fake order records from a Kafka topic and writing them into a DynamoDB table.