Stop Asking 'Kafka or Spark Streaming?' It's the Wrong Question

There is a question I get asked regularly by engineers stepping into the streaming world: should I use Kafka or Spark Streaming? It’s an understandable question. The two technologies often appear together in architecture diagrams, both deal with data in motion, and both have a reputation for complexity. But the question itself contains a category error, and unpacking that is the most useful place to start.

Kafka is a distributed message broker. Spark Streaming is a processing engine. One moves data; the other transforms it. They’re not alternatives. They’re different layers of the same stack.

What Kafka actually is

Kafka’s core abstraction is a distributed, durable, ordered log. Producers write events to topics. Consumers read from those topics, at their own pace, from wherever they left off. Events are retained for as long as you configure (hours, days, indefinitely), which means consumers can replay the log, catch up after an outage, or start from the beginning to rebuild state.

What Kafka does not do is compute. It can route, fan-out, and buffer. With Kafka Streams or ksqlDB, it can handle simple transformations close to the broker. But if you need windowed aggregations across a large event stream, enrichment against a multi-gigabyte reference dataset, or ML inference per event, Kafka alone is not the right tool. That work belongs to a processing layer.

Kafka concept	What it means in practice
Topic	A named, ordered log of events
Partition	The unit of parallelism and ordering
Consumer group	Multiple consumers sharing the read load
Offset	Where a consumer is in the log (rewindable)
Retention	How long events are kept (hours to forever)

What Spark Streaming actually is

Spark Structured Streaming treats a stream as an unbounded table. You write transformations against it using the same DataFrame API you’d use for batch work, and Spark handles the mechanics of executing those transformations continuously as new data arrives. The mental model is appealing, especially for teams who already use Spark for batch processing and don’t want to maintain two separate paradigms.

The tradeoff is latency. Spark’s default execution model is micro-batch: it accumulates events for a short window, then processes them together. This introduces some delay relative to true event-at-a-time processing. For most analytical use cases (aggregations, reporting, feature generation) this is completely acceptable. For something requiring sub-hundred-millisecond end-to-end latency, it matters more.

Spark Streaming concept	What it means in practice
Structured Streaming	Stream treated as an unbounded DataFrame
Micro-batch	Events batched in small windows before processing
Watermark	How late data is tolerated in event-time windows
Checkpoint	Where Spark saves state to recover from failure
Trigger	How often Spark processes the next micro-batch

How they compare

The confusion makes sense on the surface: both deal with data in motion, both appear in the same architecture diagrams, both have reputations for operational complexity. But the responsibilities are different enough that it’s worth laying them out directly.

	Kafka	Spark Streaming
Primary role	Message transport & storage	Data processing & computation
Stores data?	Yes (configurable retention)	No, processes and forwards
Computes/transforms?	Minimally (via Kafka Streams)	Yes, this is its whole purpose
Latency	Milliseconds	Seconds (micro-batch)
Replay past events?	Yes, by offset	Only if reading from Kafka
Scales by	Partitions & brokers	Executors & partitions
Fault tolerance	Replication across brokers	Checkpointing & lineage
Good for	Fan-out, buffering, delivery	Aggregations, joins, ML inference

How they fit together

The pattern that works well in practice is straightforward. Kafka sits at the front: ingesting events from producers, buffering them durably, and making them available to any number of downstream consumers. Spark Streaming sits downstream: reading from Kafka topics, computing over the data, and writing results to a sink: a database, object storage, or another Kafka topic that downstream systems read from.

This separation of concerns is the right abstraction. Kafka is responsible for reliable delivery and ordering guarantees. Spark is responsible for what you do with the data once you have it. Each layer can scale independently, fail independently, and be reasoned about independently.

A bug in the Kafka layer is a delivery problem. A bug in the Spark layer is a computation problem. They’re different failure modes with different debugging approaches, and keeping them separate makes the system easier to operate than a monolith that tries to do both.

Choosing between them

The practical guidance is this. If you’re routing events between services, building a pub/sub architecture, or need consumers to work at their own pace with replay capability, that’s Kafka’s job. If your processing is simple enough (filtering, lightweight enrichment, basic aggregation), Kafka Streams can handle it without introducing a separate cluster.

If you need to compute something complex (multi-source joins, time-windowed analytics, stateful sessionization, model inference), add Spark. If your team already runs Spark for batch, the marginal cost of adding Structured Streaming is low. If you’re starting from scratch and your processing needs are simple, it may not be worth the operational overhead.

Scenario	Reach for
Route events between microservices	Kafka
Fan-out to multiple consumers	Kafka
Replay historical events	Kafka
Simple filtering or lightweight aggregation	Kafka Streams
Windowed aggregations over event time	Spark Streaming
Join a stream against a large lookup table	Spark Streaming
ML inference on a live stream	Spark Streaming
Team already uses Spark for batch	Spark Streaming
Sub-100ms latency is a hard requirement	Kafka (Spark adds overhead)

What I’d caution against is making this choice on the basis of what’s popular in job postings or what appears on the most conference talk slides. Both tools have real operational weight. Running Kafka well means understanding replication, partition balance, and consumer lag. Running Spark Streaming well means understanding checkpointing, watermarks, and resource allocation. Neither is plug-and-play at scale.

The thing worth remembering

Kafka and Spark Streaming solve adjacent problems and compose well together, which is why they appear together so often. But they are not interchangeable, and the question of which to use usually has a clear answer once you’re precise about what you’re trying to do.

Are you moving data between systems and need durability, fan-out, and replay? That’s Kafka. Are you computing something non-trivial over a data stream? That’s Spark. Are you doing both? Run them together, with a clean interface between them, and treat each one as the specialist it is.

The complexity in streaming architectures rarely comes from picking the wrong tool. It comes from not being clear enough about what each tool is for.