Kafka vs Spark Streaming

Apache Kafka and Spark Streaming are complementary technologies, not competitors. Kafka is a messaging platform that stores and delivers events. Spark Streaming is a processing engine that computes on data streams. Most real-time architectures use both.

Different Layers of the Stack

Kafka sits at the messaging layer. It ingests events from producers, stores them durably, and delivers them to consumers. It does not compute aggregations, join streams, or run ML models (beyond basic processing via Kafka Streams).

Spark Streaming sits at the processing layer. It reads data from sources (including Kafka), performs transformations, aggregations, windowing, ML inference, and writes results to sinks (databases, data lakes, dashboards).

Processing Models

Spark Structured Streaming uses micro-batching — accumulating data for a small interval (100ms to seconds) and processing it as a batch. This delivers higher throughput at the cost of latency. Continuous processing mode reduces latency but is still maturing.

Kafka Streams processes records one at a time as they arrive, achieving lower per-record latency. But it lacks Spark's analytical power — no SQL engine, no ML integration, no DataFrames.

The Standard Architecture

The typical real-time data pipeline:

1. Producers → Events published to Kafka topics
2. Spark Streaming reads from Kafka topics
3. Spark performs transformations, aggregations, enrichments
4. Results written to data lake, database, or back to Kafka

This architecture leverages Kafka's durability and Spark's processing power. For system design interviews, understanding this layered architecture is essential.

See our stream processing concepts and interview questions for common real-time pipeline patterns.