Change data capture (CDC) is a data integration pattern to track changes in a database so that actions can be taken using the changed data. Debezium is probably the most popular open source platform for CDC. Originally providing Kafka source connectors, it also supports a ready-to-use application called Debezium server. The standalone application can be used to stream change events to other messaging infrastructure such as Google Cloud Pub/Sub, Amazon Kinesis and Apache Pulsar. In this post, we develop a CDC solution locally using Docker. The source of the theLook eCommerce is modified to generate data continuously, and the data is inserted into multiple tables of a PostgreSQL database. Among those tables, two of them are tracked by the Debezium server, and it pushes row-level changes of those tables into Pub/Sub topics on the Pub/Sub emulator. Finally, messages of the topics are read by a Python application.

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.

We'll discuss a Change Data Capture (CDC) architecture with a schema registry. As a starting point, a local development environment is set up using Docker Compose. The Debezium and Confluent S3 connectors are deployed with the Confluent Avro converter and the Apicurio registry is used as the schema registry service. A quick example is shown to illustrate how schema evolution can be managed by the schema registry.

Change data capture (CDC) on Amazon MSK and ingesting data using Apache Hudi on Amazon EMR can be used to build an efficient data lake solution. In this post, we'll build a Hudi DeltaStramer app on Amazon EMR and use the resulting Hudi table with Athena and Quicksight to build a dashboard.

Change data capture (CDC) on Amazon MSK and ingesting data using Apache Hudi on Amazon EMR can be used to build an efficient data lake solution. In this post, we'll build CDC with Amazon MSK and MSK Connect.

Change data capture (CDC) on Amazon MSK and ingesting data using Apache Hudi on Amazon EMR can be used to build an efficient data lake solution. As a starting point, we’ll discuss the source database and CDC streaming infrastructure in the local environment.