The Table API is not a new kid on the block. But the community has worked hard on reshaping its future. Today, it is one of the core abstractions in Flink next to the DataStream API. The Table API can deal with bounded and unbounded streams in a unified and highly optimized ecosystem inspired by databases and SQL. Various connectors and catalogs integrate with the outside world.

But this doesn't mean that the DataStream API will become obsolete any time soon. This repository demos what Table API is capable of today. We present how the API solves different scenarios:

• as a batch processor,
• a changelog processor,
• a change data capture (CDC) hub,
• or a streaming ETL tool

with many built-in functions and operators for deduplicating, joining, and aggregating data.

It shows hybrid pipelines in which both APIs interact in symbiosis and contribute their unique strengths.

1. Import this repository into your IDE (preferably IntelliJ IDEA). Select the pom.xml file during import to treat it as a Maven project. The project uses the latest Flink 1.15.

2. All examples are runnable from the IDE. You simply need to execute the main() method of every example class.

3. In order to make the examples run within IntelliJ IDEA, it is necessary to tick the Add dependencies with "provided" scope to classpath option in the run configuration under Modify options.

4. For the Apache Kafka examples, download and unzip Apache Kafka. Start up Kafka and Zookeeper:

   ./bin/zookeeper-server-start.sh config/zookeeper.properties &
   
   ./bin/kafka-server-start.sh config/server.properties &
   

   Run FillKafkaWithCustomers and FillKafkaWithTransactions to create and fill the Kafka topics with Flink.

5. For the MySQL CDC example, run StartMySqlContainer with an available Docker setup to set up a dummy database instance. FillMySqlWithValues provides a Flink job to update the database tables while the CDC example is running.