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Head-to-head · Stream Processing Software

Kafka Streams vs Apache Spark

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score Kafka Streams #5 in Stream Processing Software 8.9/10 Free plan Free plan✓ 2 of 3 features Visit Kafka Streams
Apache Spark #8 in Stream Processing Software 8.6/10 Free plan Free plan✓ 0 of 3 features Visit Apache Spark

Kafka Streams leads on 0 checks, Apache Spark on 0, and 4 are even. Who comes out ahead on the 4 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreKafka Streams · 8.9/10
  • Free planboth
  • Most featuresKafka Streams · 2 of 3

Kafka Streams scores higher on our rubric for stream processing software: 8.9 against 8.6 out of 10; our editors rank them #5 and #8.

Kafka Streams is the better fit for kafka-native Java and Scala applications. Apache Spark is the better fit for mature teams combining batch and streaming.

  • Kafka Streams fits best

    Kafka-native Java and Scala applications

  • Apache Spark fits best

    Mature teams combining batch and streaming

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Side by side

Feature Kafka Streams 8.9/10 Visit ↗ Apache Spark 8.6/10 Visit ↗
At a glance
Editor score 8.9 8.6
Ranking #5 in Stream Processing Software #8 in Stream Processing Software
Best for Kafka-native Java and Scala applications Mature teams combining batch and streaming
Pricing model Free Free
Starting price Free plan Free plan
Free plan ✓ ✓
Free trial — —
Deployment Self-hosted, Cloud Self-hosted
Platforms Windows, macOS, Linux Windows, macOS, Linux
Support Community, Docs Community, Docs
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Kafka Streams 2/3 · Apache Spark 0/3
SQL processing — Not checked yet
Stateful processing ✓ (best) Not checked yet
Event-time windows ✓ (best) Not checked yet
Specs
Deployment model Self-hosted Not checked yet
Supported languages Java, Scala Not checked yet
Source connectors Not checked yet Not checked yet
Our review
Pros
  • Stateful transformations, aggregations, joins, and four window types
  • Exactly-once processing semantics for Kafka-based applications
  • Runs self-hosted or in cloud environments without a separate processing cluster
  • Structured Streaming supports incremental stream processing
  • Broad APIs across Python, SQL, Scala, Java, and R
  • Connects to major data sources, clusters, and lakehouse systems
Cons
  • Core integration scope is centered on Apache Kafka
  • No SQL support for stream-processing workflows
  • Support is limited to community channels and documentation
  • Self-hosted deployment requires cluster infrastructure
  • Streaming is one part of a broader batch-focused platform
  • Not a hosted ETL SaaS product with subscription tiers
Our verdict

Kafka Streams is an open-source client library for building real-time applications and microservices whose input and output data are stored in Apache Kafka clusters. It is designed for developers and teams creating event-driven services,…

Read the review →

Apache Spark is an open-source distributed computing engine for large-scale data processing and analytics. It is aimed at developers, data engineers, analysts, and organizations processing data across clusters. Structured Streaming…

Read the review →
  1. Kafka StreamsStream Processing Software 8.9Free plan
  2. Apache SparkStream Processing Software 8.6Free plan

Strengths and trade-offs

  • Kafka Streams — where it wins

    • Stateful transformations, aggregations, joins, and four window types
    • Exactly-once processing semantics for Kafka-based applications
    • Runs self-hosted or in cloud environments without a separate processing cluster

    Where it doesn't

    • Core integration scope is centered on Apache Kafka
    • No SQL support for stream-processing workflows
    • Support is limited to community channels and documentation

  • Apache Spark — where it wins

    • Structured Streaming supports incremental stream processing
    • Broad APIs across Python, SQL, Scala, Java, and R
    • Connects to major data sources, clusters, and lakehouse systems

    Where it doesn't

    • Self-hosted deployment requires cluster infrastructure
    • Streaming is one part of a broader batch-focused platform
    • Not a hosted ETL SaaS product with subscription tiers

More comparisons

All stream processing software comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update