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Apache Flink vs Apache Spark

Flink processes events one at a time for true low-latency streaming; Spark uses micro-batches with a far larger ecosystem. Many stacks run both.

Side by side

Apache Flink Apache Spark
Vendor Apache Software Foundation Apache Software Foundation
Pricing model Open source + paid options Open source + paid options
Free tier Yes Yes
Deployment Self-hosted Self-hosted
Open source Yes (Apache-2.0) Yes (Apache-2.0)
Best for Teams building real-time, low-latency streaming pipelines that feed operational or analytical stores. Data engineering teams building large-scale batch ETL, streaming or machine-learning pipelines.
Pricing

Free and open source under the Apache Software Foundation; managed hosting is available through third-party cloud platforms.

Pricing has not been verified yet — see the vendor's site.

Free and open source under the Apache Software Foundation; managed lakehouse platforms built on Spark, such as Databricks, are priced separately.

Pricing has not been verified yet — see the vendor's site.

Features
  • True event-at-a-time stream processing with exactly-once guarantees
  • Event-time processing and watermarking
  • Stateful processing with built-in checkpointing
  • Flink SQL for declarative pipeline authoring
  • Unified batch and streaming model
  • High-throughput, low-latency execution
  • Unified batch, SQL, streaming and ML APIs
  • In-memory distributed execution (RDDs/DataFrames)
  • Structured Streaming for near-real-time pipelines
  • MLlib for distributed machine learning
  • Runs on Kubernetes, YARN or standalone
  • Broad connector ecosystem for storage and lakehouse formats

Verdict

The architectural difference between these two decides the comparison more than any feature list. Apache Flink processes events one at a time, with stateful, exactly-once processing, event-time semantics and millisecond-scale latency — it treats batch as a special case of streaming, not the other way around. Apache Spark's Structured Streaming runs in micro-batches: fast, and good enough for most near-real-time needs, but not the same thing as Flink's per-event model. Spark's advantage is everything around that core: a far larger and more mature ecosystem spanning batch ETL, SQL and machine learning, plus a bigger hiring pool and, through Databricks, a managed platform spanning all of it.

  • You need true low-latency, stateful stream processing — fraud detection, or real-time ETL feeding an OLAP store like ClickHouse or Druid.
  • Event-time processing and watermarking are requirements, not nice-to-haves.
  • Exactly-once guarantees under continuous, high-throughput streams matter to correctness.

Choose Apache Spark if

  • The workload is primarily batch ETL, SQL analytics, or machine-learning training rather than continuous low-latency streaming.
  • You want the most mature connector ecosystem, documentation, and hiring pool of any engine in this category.
  • Near-real-time (seconds, not milliseconds) streaming is acceptable, and Structured Streaming's micro-batch model is enough.
  • You want the option of one managed platform, via Databricks, spanning engineering, ML and SQL.

What they share

Both are open-source under Apache-2.0, self-hosted by default, commonly deployed on Kubernetes, and both integrate with Kafka as a primary streaming source. Neither is typically the end-user-facing product in a stack — both sit inside a larger pipeline.

The honest caveat

Many teams run both rather than picking one exclusively — Spark for batch ETL and ML, Flink specifically for the low-latency streaming layer feeding a real-time OLAP database. The "vs" framing here understates how often the honest answer is "both, for different jobs," and it's worth sizing the actual latency requirement of the streaming piece before assuming you need Flink's added operational complexity at all.

Last reviewed September 22, 2026

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