Choose Apache Spark if your team mostly runs batch jobs, SQL analytics and machine learning, and wants streaming in the same engine with Structured Streaming. Choose Apache Flink if streaming is the main event and you need large state, event-time handling and savepoints for long-running jobs. Spark treats streaming as batch-like queries; Flink treats batch as a bounded stream.
I read Apache Spark's home page, FAQ and docs, then Apache Flink's home, architecture and use-case pages. Both are open source and both do batch and streaming, so the difference is in what each one was designed around.
Spark and Flink side by side
| Apache Spark | Apache Flink | |
|---|---|---|
| Designed around | Data engineering, data science and machine learning | Stateful computations over data streams |
| Streaming model | Structured Streaming, built on Spark SQL | Unbounded streams processed continuously |
| Batch | Core strength | Processed as bounded streams |
| Languages | Python, SQL, Scala, Java, R | SQL, DataStream API, ProcessFunction |
| State and recovery | Spills to disk when data doesn't fit in memory | Very large state, incremental checkpoints, savepoints |
| Runs on | Standalone, YARN or Kubernetes | YARN, Kubernetes or standalone |
| Free option (as of October 2026) | Yes, open source | Yes, open source |
Where Spark fits best
Spark calls itself a multi-language engine for data engineering, data science and machine learning on single machines or clusters. You can work in Python, SQL, Scala, Java or R, run distributed ANSI SQL for dashboards and ad hoc reports, and train models on a laptop before scaling the same code to thousands of machines. The project says 80% of the Fortune 500 use it and more than 2,000 people have contributed.
For streaming, Spark's FAQ is clear: use Structured Streaming. It's built on the Spark SQL engine and lets you write a streaming job the same way you'd write a batch job on static data. The older Spark Streaming, with its DStreams, is legacy and no longer updated, which is worth knowing if you're reading old tutorials.
Where Flink is the stronger choice
Flink's architecture page starts from streams. Unbounded streams have a start but no end, so they must be processed as events arrive, while bounded streams are what most people call batch. Flink offers exactly-once state consistency, event-time processing and handling of late data, which matter when events arrive out of order.
Its standout feature for long-running apps is the savepoint, a consistent image of an application's state you can use to restart, upgrade or move a job without losing its place. Flink also manages very large state, up to several terabytes on its use-case page, and ships a library for spotting patterns in event streams.
Flink usually pairs with Kafka, as our Kafka vs Flink comparison explains.
The question that settles it
Ask how your data arrives and how quickly you must act on it. If results every few minutes or hours are fine and you also need SQL and machine learning, Spark covers it in one engine. If you're reacting to each event as it lands, keeping state for every customer or device, and you can't lose your place during upgrades, Flink was built for that job.
Spark is also compared with Hadoop in our Spark vs Hadoop comparison.
How to decide
- Mostly batch ETL, SQL and ML? Spark.
- Event-driven apps or fraud and anomaly detection on live streams? Flink.
- Python-first data scientists? Spark's PySpark is a pip install away.
- Jobs that must survive upgrades with their state intact? Flink's savepoints.
- Two kinds of work in one company? You can run Spark for analytics and Flink for real-time apps side by side.
If you're building the skills behind this, see these ChatGPT prompts for SQL and this rundown of data analytics tools.
Want AI doing the heavy lifting in your marketing?
I build the systems that handle the boring 80 percent, so you get your week back. Done properly, with the human kept in.
My advice on Spark or Flink
If your data team spends most of its time on batch pipelines, analytics and machine learning, Spark is the more versatile engine and its Structured Streaming handles a lot of real-time work. If your product depends on reacting to events as they happen, with large state and zero tolerance for losing it, Flink is the engine designed for that. I'd prototype your hardest streaming job in both before you commit.
Do you make data processing tools? Tool makers can ask to be considered for a featured place in comparisons like this one.
Frequently asked questions
Is Flink faster than Spark?
Neither project's pages give a head-to-head number. Flink highlights low latency and in-memory computing for streams; Spark highlights fast distributed SQL and machine learning at scale.
Does Spark do real-time streaming?
Yes. Spark's FAQ says Structured Streaming is its current streaming engine, built on Spark SQL. The older Spark Streaming is legacy and no longer updated.
Can Flink do batch processing?
Yes. Flink treats batch as bounded streams and says it processes them with algorithms designed for fixed-size data sets.
What is a Flink savepoint?
Flink describes a savepoint as a consistent image of an application's state that you can use to restart, update or migrate the application.
Do I need Hadoop for Spark?
No. Spark's FAQ says it doesn't need Hadoop, though on a cluster you'll need some form of shared file system.
