Building a Scalable Search Architecture | Confluent

This post details the technical implementation of building a scalable search architecture using Kafka Connect. It explains how to use the JDBC Source Connector to incrementally ingest data from relational databases (MySQL example provided) into Kafka topics, covering different operational modes (incrementing, timestamp, combination, custom query) and the importance of JDBC drivers and schema evolution with Schema Registry. It then describes the setup and configuration of the Elasticsearch Sink Connector to index data from Kafka topics into Elasticsearch, highlighting considerations for mappings and dynamic fields. The post advocates for a Kafka-centric approach to decouple data producers and consumers, improving scalability and flexibility.