Query engines & federation · Apache Software Foundation

Apache Flink

Open-source stream-processing engine for stateful, low-latency event processing at scale, with batch support.

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Apache Flink is an open-source distributed processing engine built primarily for stateful stream processing, handling unbounded event streams with exactly-once processing guarantees, event-time semantics, and millisecond-scale latency. It treats batch processing as a special case of streaming, which distinguishes its architecture from Spark's batch-first, streaming-as-micro-batch model. Flink is commonly used for real-time ETL feeding OLAP stores such as ClickHouse or Druid, fraud detection, and continuous aggregation pipelines, using its own runtime for checkpointing and state management rather than relying on an external store. It integrates with Flink SQL for declarative pipeline authoring alongside its lower-level DataStream API in Java, Scala and Python. Flink is self-hosted, typically on Kubernetes or YARN; managed hosting is available through cloud vendors and platforms such as Confluent and Amazon Managed Service for Apache Flink rather than a single dominant commercial vendor.

At a glance

Vendor Apache Software Foundation
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (Apache-2.0)
Best for Teams building real-time, low-latency streaming pipelines that feed operational or analytical stores.

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.

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

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Apache Spark Open-source distributed processing engine for large-scale batch, SQL, streaming and machine-learning workloads. Ray Open-source Python framework for distributing machine-learning training, tuning and general compute across a cluster. Trino Open-source distributed SQL engine that queries data across warehouses, lakes and databases without moving it.

Apache Flink in the index now

Terms to know

Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. ETL (extract, transform, load) A data integration pattern that transforms data before loading it, unlike ELT which transforms after loading. Batch processing Processing data in large, scheduled groups rather than continuously as each record arrives. Exactly-once processing A processing guarantee that each event affects the result exactly once, even after retries or redelivery. Fraud detection Identifying transactions, claims, or behavior that are likely fraudulent, using rules, statistics, or machine learning. OLAP (online analytical processing) A category of technology optimized for fast, multi-dimensional aggregation and slicing of data for analysis.

Related guides

How to choose a distributed query engine This category bundles two different jobs — federating SQL across existing systems, and distributed compute for pipelines and ML — start by telling them apart.