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Big Data & Distributed Computing
25 minintermediate

Kafka Connect for Source and Sink Connectors

Kafka Connect is a scalable, fault-tolerant framework for streaming data between Kafka and external systems without writing custom consumer or producer code. Source connectors pull data from external systems — databases, file systems, REST APIs — and publish it to Kafka topics. Sink connectors consume messages from Kafka topics and write them to external systems — databases, data warehouses, S3, Elasticsearch. The Connect framework handles parallelism, fault tolerance, offset tracking, and schema management automatically, reducing a complex integration task to a configuration exercise.

Kafka Connect is the correct architectural choice whenever the integration task is moving data between Kafka and a well-supported external system without custom transformation logic. Writing a custom Kafka producer to read from a PostgreSQL table and write to Kafka is reinventing the Debezium or JDBC Source Connector — which already handles incremental loading, CDC via WAL replication, schema registration, and fault-tolerant offset tracking. Custom producers should be reserved for sources with no available connector, novel authentication requirements, or transformations that the Single Message Transform framework cannot express.

Analogy🏏Cricket
🏏 Think of it like cricket: Imagine the DRS review system deployed across three independent video-review centres in Mumbai, Chennai, and London, each holding a copy of the ball-tracking data. A CAP partition is a network outage that cuts communication between them. A CP system says: if the centres cannot synchronise, no review decision is issued — no player walks until the system is restored. Consistency is guaranteed; availability is sacrificed. An AP system says: each centre issues its own decision based on its local data, even if that means Mumbai says out and London says not out — reviews continue but different centres may give different verdicts. Partition tolerance is non-negotiable because the network always has the possibility of failing; the choice is whether umpires wait for consensus or proceed with local data.
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