Kafka Event Streaming Pipeline

What this diagram shows

A Kafka event streaming pipeline captures change data from source systems via CDC (Change Data Capture), routes it into a three-broker Kafka cluster for durability and scalability, processes events through stream processors like Kafka Streams or Flink, and distributes the results to analytical sinks such as data warehouses, data lakes, or search indices. Data flows from CDC connectors into Kafka topics, where consumer applications transform and enrich events in real time, then write aggregated or processed data to downstream systems for analytics and reporting. The three-broker cluster ensures fault tolerance and load balancing across partitions.

Key components

• CDC Source (Debezium/Maxwell) — Captures row-level changes from source databases (PostgreSQL, MySQL, MongoDB) and publishes them as change events into Kafka topics.
• Kafka Broker Cluster (3 nodes) — Stores and replicates events across three brokers to guarantee persistence, durability, and high availability with configurable replication factor.
• Kafka Topics — Organises events by domain or data source (e.g. orders-cdc, users-cdc) and maintains ordering within partitions for consistent event replay.
• Stream Processor (Kafka Streams/Apache Flink) — Consumes events from topics, applies transformations, aggregations, joins, and filters to enrich or normalise data in real time.
• State Store — Maintains local or remote state for stream processors to perform stateful operations like windowed aggregations and joins without querying external systems.
• Analytical Sink (Snowflake/BigQuery/Elasticsearch) — Receives processed events and stores them in formats optimised for analytics queries, dashboarding, and search.
• Schema Registry — Manages and validates event schemas (Avro, Protobuf, JSON Schema) to ensure schema compatibility across producers and consumers.

When to use it

Use this diagram when designing real-time data pipelines where you need to synchronise changes from operational databases into analytics platforms without batch delays. It applies when you require event durability, replay capability, and multiple consumer applications processing the same events independently. Suitable for organisations building event-driven architectures, real-time dashboards, audit trails, or data lake ingestion from transactional systems. Appropriate when you have multi-team setups where different teams consume the same event stream for different purposes.

Common mistakes

• Treating Kafka as a data warehouse instead of a real-time message broker, leading to queries directly against topics rather than materialized sinks.
• Ignoring schema evolution and omitting Schema Registry, causing downstream consumers to break when event fields change or new producers emit incompatible formats.
• Designing a single-broker Kafka cluster to reduce cost, which removes fault tolerance and makes planned maintenance impossible without downtime.

Adapting it to your system

Identify your source systems (databases, APIs, logs) and select the appropriate CDC tool (Debezium for databases, custom producers for applications). Map each source to a Kafka topic with a naming convention reflecting domain or data class. Configure the broker cluster with replication factor matching your availability requirements, typically three for production. Choose your stream processor based on complexity (Kafka Streams for simple transformations, Flink for complex stateful logic). Identify downstream sinks by consumer needs, whether analytics, full-text search, caching, or operational dashboards. Include Schema Registry if you have multiple producer teams or evolving schemas. Adjust partitioning strategy based on expected volume and parallelism requirements.