Kafka vs Apache Beam: Streaming Compared
- Kafka (Apache Kafka / Confluent Cloud): Distributed log and messaging system. Handles durable event storage, replication, and pub/sub delivery semantics.
- Apache Beam (plus runners such as Dataflow/Flink/Spark): Programming model for expressing batch/stream transformations; runs atop execution engines that consume from sources like Kafka.
When to Use Kafka
- Capture ordered event streams with high throughput (>MB/s per partition).
- Provide durable event retention, replay, and consumer groups.
- Integrate a broad ecosystem of connectors (Kafka Connect, ksqlDB, Kafka Streams).
When to Use Beam/Dataflow
- Build portable data pipelines that can run on multiple runners.
- Apply complex windowing, stateful processing, joins, and aggregations without locking into a single stream processor.
- Execute unified batch and streaming logic with one code path.
Typical Architecture
Producers → Kafka topics → Beam pipeline (Dataflow/Flink) → Sinks (BigQuery, Pub/Sub, GCS, etc.)
Kafka stores and distributes events; Beam processes and derives insights.
Decision Checklist
| Question | Choose Kafka | Choose Beam/Dataflow |
|---|---|---|
| Do you need event storage + fan-out consumers? | ✅ | ❌ |
| Do you need complex data transforms across engines/clouds? | ❌ | ✅ |
| Are you standardising on managed services? | Confluent Cloud / MSK | Google Cloud Dataflow / Flink |
| Is SQL stream processing required? | ksqlDB | Beam SQL (with runner support) |
Complementary Use
Most production systems combine both: Kafka for ingestion + buffering, Beam/Dataflow for enrichment and delivery. Evaluate managed offerings (Confluent Cloud, Amazon MSK, Google Dataflow) if you prefer SaaS operations.