Move away only if running the cluster, tuning state or working Java-first takes more effort than your streaming jobs are worth. Simple pipelines on a message log suit libraries that live inside your application. Teams already on batch tooling suit unified engines. People who'd rather write SQL than jobs suit streaming databases. And portable APIs suit anyone worried about lock-in.
Apache Flink is a distributed engine for stateful computation over data streams. It promises exactly-once state consistency and event-time handling that copes with late data, and adds savepoints, incremental checkpoints and high-availability setups. Data engineers usually look around when all that power comes with more operational work than their use case needs.
Here are seven options, grouped by where the processing runs, because that decides how much of your week goes on operations.
How the seven differ
| Option | What you swap | Leans on |
|---|---|---|
| Apache Beam | API | One model, many runners |
| Apache Samza | Engine | Stateful processing from Kafka and others |
| Apache Storm | Engine | Any programming language |
| Kafka Streams | Approach | Java or Scala library |
| Materialize | Approach | SQL |
| RisingWave | Approach | Real-time ingestion |
| Spark Structured Streaming | Engine | The same Spark APIs |
Read the list by deployment model as much as by features. Four of the seven can run without a separate processing cluster you look after yourself: Kafka Streams inside your app, Samza as a library, and Materialize and RisingWave as hosted products. The rest assume you, or a provider, keep a cluster healthy.
Libraries that live inside your service
These two shrink the operations job the most. Processing runs inside an application you already deploy, so there's no separate engine to patch, scale and monitor at night.
Kafka Streams
A client library for apps and microservices whose inputs and outputs live in Kafka. No extra processing cluster is needed, and you write it as ordinary Java or Scala.
Apache Samza
A framework for stateful stream processing that reads real-time feeds from sources including Apache Kafka. It belongs in this group when run as a standalone library; it can also be deployed on YARN or Kubernetes, which brings a cluster back into the picture.
Engines you run as a cluster
Pick from this pair when you still want a dedicated processing engine but a different one. You keep the cluster, so the gain has to come from the language, the API or what your team already knows.
Apache Storm
A free, open source realtime computation system spread across machines, usable from any programming language.
Spark Structured Streaming
Spark's answer to streaming. Pipelines reuse the same Spark APIs, and a real-time mode and a micro-batch mode run from one codebase, which helps teams that already know the engine from other work.
Streaming databases
These change who does the work. Materialize lets people who know SQL shape live data themselves instead of engineers writing and deploying jobs, which suits teams where analysts outnumber stream engineers.
Materialize
Turns data from many sources into live, queryable data products using SQL, delivered as SaaS or as an air-gapped install on your own premises.
RisingWave
Pulls from event streams, databases and data lakes and processes the data as it arrives, with a hosted product for teams who'd rather not run it themselves.
Where Apache Beam fits
Beam is the odd one out. Its own site describes one programming model for both batch and streaming, with pipelines handed to execution environments it calls runners. In other words, it sits above engines like Flink rather than replacing them.
That's handy if portability is the goal: write a pipeline once and keep the choice of where it executes. It's no help if the goal is to stop operating a cluster, because something underneath still has to do the work.
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Keep the same question in mind for every option: are you swapping the engine, the API, or the whole approach? Mixing those up is how migrations stall halfway.
Match your reason to a group
- Tired of running a cluster for simple Kafka pipelines: Kafka Streams runs inside your own service.
- A Spark shop already: Structured Streaming reuses what you know.
- Analysts want answers, not jobs: Materialize puts SQL at the front; RisingWave processes streams, databases and lakes in real time.
- Wary of committing to any engine: Beam keeps the runner choice open.
- Need language freedom: Storm works from any language.
- Already on Kubernetes or YARN: Samza deploys onto either, or runs as a library.
What you give up when you leave
Be honest about which of today's guarantees you rely on. Exactly-once state, event-time windows that cope with late records, savepoints you can restore from: if any of those quietly keep your numbers right, write them down and check each candidate's documentation for its equivalent before you go further.
If none of them matter, that's useful too. A lot of streaming work is simple enrichment or routing, and a library inside your own service may be all you ever needed.
Watch for the opposite trap. If you leave for a simpler tool and then find yourself writing your own checkpointing in application code, stop and reconsider: you're rebuilding what you left behind.
Two pages go deeper on the most common comparisons: how Spark's streaming compares and where Kafka ends and stream processing begins.
Keep or leave: a quick check
| Keep Flink if | Look elsewhere if |
|---|---|
| Stragglers arriving hours late still have to count | Most jobs just tag, filter or route records |
| A savepoint is your safety net on deploy day | Analysts outnumber stream engineers |
| A double-counted event would break a report | Kafka is already the start and end of every job |
| Developers drop from SQL to ProcessFunction regularly | Nobody wants to bet on a single engine |
If most of your ticks land in the left column, staying is the sensible call. If they land on the right, take one real job with state in it, rebuild it in the matching candidate, and run it beside the current version on the same input until the outputs agree through a restart. Count the hours spent keeping it running as well as the hours spent writing it.
Operations questions to ask every candidate
- How do you upgrade it without losing state or reprocessing everything?
- What happens to in-flight data when a machine fails?
- Where does state live, and how big can it grow before you need to tune it?
- Who on your team can debug it at 2am, and in what language?
- How do you replay a day of input after fixing a bug in the logic?
- What does it need from you to scale up for a busy week, and back down afterwards?
Ask these of Flink too, as you run it today. If your current answers are good, the bar for any replacement goes up accordingly.
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Frequently asked questions
Can I test Spark streaming on my laptop?
Yes. Spark runs on single-node machines as well as clusters, so a local trial is a normal first step.
Does Flink handle batch as well as streams?
Yes. It processes bounded and unbounded streams, and its SQL layer works on stream and batch data.
Is RisingWave open source?
It offers an open-source download alongside its hosted product.
Can Beam run my existing jobs unchanged?
No. It's a separate programming model, so pipelines are rewritten against its API and then handed to a runner.
Can I avoid Java and Scala entirely?
Storm accepts any language, and Spark is a multi-language engine.
