- Older Kafka-plus-Spark tutorials describe the previous engine
- Side by side
- Choose Kafka when the job is moving and keeping events
- Choose Spark when the job is computing on the data
- How the two usually share a pipeline
- Who runs what: brokers, connectors and jobs
- Common mix-ups when the two meet
- What a first proof of concept should prove
- A seven-step pipeline plan
- Frequently asked questions
Kafka and Spark do different jobs and often work side by side. Choose Kafka when you need a durable log that moves and retains event streams between systems. Choose Spark when you need an engine to transform, analyse or train models on data, whether in batches or as a stream. Most teams asking this question end up needing to decide where each one belongs.
Data engineers usually hit this question after spotting both names in the same architecture diagram and wondering whether one could replace the other. Apache Kafka and Apache Spark get mentioned together so often that it's easy to assume they compete. Mostly, they don't. One keeps and moves events. The other computes on data.
Below is what each one is built for, the trap in older tutorials that pair them, and the steps to take before you put either into a pipeline.
Older Kafka-plus-Spark tutorials describe the previous engine
Here's the detail that catches people out. Spark's documentation labels the original Spark Streaming, the one built on DStreams, as the previous generation, and steers new work towards Structured Streaming, which it describes as a scalable and fault-tolerant stream processing engine built on the Spark SQL engine.
A lot of blog posts and courses about reading Kafka topics into Spark were written for that older API. They may still run, but you'd be building something new on a design the project itself treats as legacy. Before you copy any example, look at which streaming API it uses. If the code talks about DStreams, hunt for a version written for the newer engine.
Checking the API generation also helps when you're hiring or reviewing someone else's code: knowing which generation a codebase uses tells you how much modernising lies ahead.
Side by side
| Kafka | Spark | |
|---|---|---|
| Describes itself as | An open-source event streaming platform | A multi-language engine |
| Built for | Keeping and moving event streams | Engineering, science and ML work on data |
| Streaming piece | The event log, held on brokers | Structured Streaming, on Spark SQL |
Read the table as two halves of one pipeline rather than two rival products: the left column keeps and moves events, the right column computes on them.
Choose Kafka when the job is moving and keeping events
Kafka is an open-source, distributed platform for event streams. Its storage layer is a set of servers called brokers, events are retained durably for whatever period you set, and Kafka Connect shifts data in and out between Kafka and other systems.
- Several teams read from one shared feed, so a single durable log beats copying data between them.
- You want to replay what happened, because the events are retained rather than thrown away once read.
- You need to feed data from databases or apps into other systems without writing bespoke glue for each one.
Choose Spark when the job is computing on the data
Spark is a processing engine you can drive from more than one programming language, on a single machine or a whole cluster. Its distributed ANSI SQL is aimed at dashboarding and ad-hoc reporting, and Structured Streaming lets the same engine handle records as they come in.
- You need to join, aggregate or clean large datasets, in batches or continuously.
- Your analysts want to work in SQL over data that's too big for one machine.
- Data scientists want to train models on the same data the engineers are preparing.
How the two usually share a pipeline
A common pattern puts Kafka at the front, collecting and retaining events from apps and databases, and Spark further along, reading those events, transforming them and writing results somewhere useful. In that picture you'd be asking which job each tool does, not which one wins.
If you're weighing Spark against another processing engine instead, our Spark and Flink comparison is the closer match. If your real question is how Kafka compares with a traditional message broker, the Kafka and RabbitMQ breakdown covers that.
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Who runs what: brokers, connectors and jobs
A Kafka deployment is several moving parts from the first day. Whoever owns Kafka is looking after retention settings, broker health and the connectors that feed it.
The Spark side looks different. Its owner worries about the jobs, the SQL queries and the models the engine runs, and about whether a job should run on a single node or a cluster. Spark's own page says it runs on either, so a small team can start on one machine and grow later. The two halves need different skills, which is why it pays to treat them as separate jobs.
Common mix-ups when the two meet
- Treating Kafka as the place where finished results live. It keeps the raw stream; processed output usually belongs in a database or warehouse.
- Assuming Spark keeps your events safe. It reads them and works on them; durable retention is the job Kafka does.
- Writing custom import code when a ready-made connector would do the same job with less to maintain.
What a first proof of concept should prove
Keep the first build small and specific. It should answer a handful of questions you can't settle on a whiteboard, not show off every feature.
- That events from your real sources arrive in Kafka and stay available for as long as you've decided they should.
- That a Spark SQL query over the processed data answers one real dashboard or ad-hoc reporting question your team has today.
- That results land where your analysts or apps can use them, in a shape they recognise.
- That someone other than the builder can restart each piece after a failure, using written notes.
A seven-step pipeline plan
- Write down whether the problem is moving data, keeping data, computing on data, or all three.
- Decide how long events must be kept and who needs to replay them.
- List every system that sends or receives data, and check whether a Kafka Connect connector exists for each.
- Decide whether your processing is batch, streaming or both, and which languages your team will use.
- If you'll stream with Spark, confirm every example and library targets the newer engine.
- Build a small proof of concept with real data volumes before you size any cluster.
- Agree who runs each piece day to day, since two systems mean two sets of upgrades and monitoring.
Spark's distributed ANSI SQL is pitched at dashboarding and ad-hoc reporting, so if your analysts already write SQL, involve them from step 4. They'll tell you quickly whether the results land in a shape they can use.
Frequently asked questions
Does Spark need machine learning to be useful?
No. Machine learning is one of three uses it names, alongside data engineering and data science, so a pipeline can lean on the SQL and streaming side alone.
Which programming languages does Spark support?
It calls itself a multi-language engine. Check its documentation for the current list before you settle on a language for the team.
Can I use SQL on streaming data?
On the Spark side, yes: Structured Streaming is built on the Spark SQL engine. For SQL directly over Kafka topics, check the Kafka documentation.
Are Kafka and Spark part of one product?
No. They're separate Apache projects with separate documentation, so plan their upgrades and support separately.
Where should a small team start?
Pick the one job that hurts most today, moving events or computing on them, and build that piece first.
