Kafka Connect, Source and Sink Connectors, and CDC Pipelines
Understand how Kafka integrates with external systems through managed connectors and how change data capture pipelines are built.
Inside this chapter
- What Kafka Connect Solves
- Source vs Sink Connectors
- Change Data Capture
- Connector Configuration Thinking
- When Connect Helps Most
- Business Example
Series navigation
Study the chapters in order for the clearest path from Kafka basics and local setup to stream processing, platform operations, cloud usage, and advanced event-driven architecture thinking. Use the navigation at the bottom to move smoothly through the full tutorial series.
What Kafka Connect Solves
Many teams need to move data between Kafka and systems such as databases, cloud storage, search engines, warehouses, or observability platforms. Writing and maintaining custom integration code for every case is expensive. Kafka Connect solves this by providing a framework for reusable connectors.
Source vs Sink Connectors
- Source connectors bring data into Kafka
- Sink connectors push Kafka data into external systems
This makes Kafka a central hub in many data architectures.
Change Data Capture
CDC pipelines capture changes from database transaction logs and publish them as events. This is one of the most impactful Kafka patterns because it enables analytics, search synchronization, audit pipelines, and downstream service projections without constant polling.
Connector Configuration Thinking
Connectors are not “set and forget.” Teams must understand throughput, retries, error topics, schema compatibility, snapshot behavior, and exactly how external systems behave under failure.
When Connect Helps Most
Kafka Connect is especially valuable when an organization wants standardized integration patterns instead of every team reinventing ingestion and export pipelines in separate codebases.
Business Example
An e-commerce company may stream order-table changes into Kafka through CDC, then use sink connectors to deliver that data to a warehouse, a search index, and a fraud analytics system. One database change can therefore power many downstream capabilities.