Kafka Connect Elasticsearch Connector in Action

The Elasticsearch sink connector helps integrate Apache Kafka with Elasticsearch, allowing users to stream data stored in Kafka into Elasticsearch for log analysis or full-text search. This integration can also be used for real-time analytics and other applications like Kibana. The Elasticsearch connector pushes events from Apache Kafka into an Elasticsearch index. It supports Elasticsearch 2.x, 5.x, 6.x, and 7.x versions. The tutorial guides users through the process of sending JSON documents from Kafka into Elasticsearch using ksqlDB for some Kafka operations. The connector configuration includes attributes like "connector.class", "tasks.max", "topics", "name", "connection.url", and "type.name". Users can also update the configuration to include a JSON converter or ignore keys and schemas, depending on their requirements. The tutorial further explains scenarios where product listings data is inserted into a MySQL database that needs to be sent into Elasticsearch for search functions. It demonstrates how to create a JDBC source connector and an Elasticsearch sink connector in such cases. Additionally, it discusses the use of TimestampRouter SMT to rotate indexes daily and handle tombstone messages. Lastly, the tutorial emphasizes the importance of tuning parameters like flush.timeout.ms, max.buffered.events, and batch.size for optimal performance from the Elasticsearch connector. It also mentions that Confluent provides a variety of sink and source connectors for popular databases and filesystems to stream data in and out of Kafka.