Spark Structured Streaming vs Apache Flink vs Kafka Streams: a decision matrix for data engineers

If you work with streaming data in a real enterprise, you know choosing the right engine isn't an academic exercise. It's a decision that determines your latency, your operational cost, your platform complexity and —most importantly— your organization's ability to operate real-time data sustainably.

Streaming platforms aren't a technical footnote. They're a fundamental piece of modern event-driven architectures and real-time data processing.

I've spent the last few years implementing streaming pipelines on Cloudera CDP (CDE, CFM) and Databricks, across banking, insurance and mining clients. I've seen teams pick Spark out of inertia, Flink for its technical promises, and Kafka Streams for its proximity to the broker. I've seen all of them regret it at least once —not because the technology was bad, but because it wasn't the right fit for their context.

The contenders

Three technologies appear frequently in enterprise architectures built on data, Kafka and distributed processing.

Apache Flink is a specialized stream processing engine, with a powerful state model and advanced tooling for event-time, windows and out-of-order event processing.

Kafka Streams is Kafka's natural extension: an embedded library that lets you build event processing applications without deploying a separate processing cluster.

Spark Structured Streaming is the Swiss Army knife: if your team already works with Spark, PySpark, DataFrames, SQL and platforms like Cloudera CDE or Databricks, the transition from batch to streaming feels much more natural.

The table below summarizes the key differences.

* ~100 ms and ~1 ms are reference values documented by Spark for its execution models. They do not represent a latency guarantee for any application or infrastructure. Continuous Processing is experimental/legacy in recent documentation and has operation restrictions. Micro-batch is the default mode and supports Structured Streaming's stateful operations.

When to use Spark Structured Streaming

Spark Structured Streaming turns a batch computation into incremental processing over continuously arriving data. The same DataFrame/Dataset programming model works with static data and streaming data, letting you reuse existing Spark components: SQL, joins, aggregations, windows and DataFrame operations.

By default, Structured Streaming uses a micro-batch model. Spark's documentation indicates this model can reach latencies around 100 ms and provides exactly-once guarantees for compatible scenarios.

Spark also offers Continuous Processing, a low-latency mode that can reach approximately 1 ms, but with at-least-once guarantees and significant restrictions on supported operations.

So it's not accurate to summarize Spark simply as "Spark = 100 ms". The reality is:

Spark Structured Streaming

Micro-batch
    ↓
~100 ms
Exactly-once
Full Structured Streaming support

Continuous Processing
    ↓
~1 ms
At-least-once
Operation restrictions

Current Spark documentation also contemplates additional low-latency processing modes, so the choice must be made based on the specific version and capabilities in use.

Spark shines brightest when you already have a Spark ecosystem. If your team writes PySpark every day, your platform is CDE or Databricks and your batch and streaming pipelines live on the same platform, Spark lets you maintain a very similar development model across both worlds.

The savings aren't necessarily in achieving the lowest possible latency. The savings can be in not having two engines, two platforms, two operational models and two completely different skill sets.

Where do the differences with Flink show up?

Spark supports stateful operations and has evolved considerably in this area. However, when the problem requires fine-grained control over keyed state, timers, out-of-order event processing, custom processing logic or complex event-time patterns, Flink offers a more specialized model.

The difference isn't "Spark has no state." The difference is: Flink offers a more granular state model oriented to event processing.

Flink provides mechanisms like:

• Keyed State
• Operator State
• Timers
• Broadcast State
• Process Functions
• State Backends
• Checkpoints
• Savepoints

This is especially useful when business logic depends on remembering and modifying information associated with each event or key.

When to pick Apache Flink

Flink was designed around stream processing and offers a particularly strong model for stateful applications. It's not accurate to reduce its advantage to a single figure like "<5 ms."

The real advantage lies in its ability to perform continuous per-event processing with fine-grained control over state, event-time, windows and fault recovery.

Actual latency depends on: event size, parallelism, number of operators, partitioning, network, serialization, backpressure, checkpoint configuration, infrastructure and topology complexity.

So instead of claiming Flink has a fixed <5 ms latency, it's more accurate to say: Flink is designed for low-latency, continuous per-event processing scenarios.

One of Flink's biggest advantages appears when you need to work with complex state:

Kafka
  │
  ▼
Flink
  │
  ├── Keyed State
  ├── Timers
  ├── Event Time
  ├── Watermarks
  ├── Windows
  └── Process Functions
  │
  ▼
Result

Flink also handles out-of-order events through event-time and watermark mechanisms, especially important in systems where event arrival doesn't necessarily match when they occurred.

Exactly-once in Flink

Checkpoints let Flink maintain state consistency and recover from failures. But checkpoint does not automatically mean end-to-end exactly-once. For end-to-end exactly-once, the source must be able to replay data and the sink must support transactional or idempotent mechanisms.

The correct comparison is: exactly-once for state via checkpoints; end-to-end exactly-once depends on the sources and sinks used.

The real cost of Flink

Flink has a higher operational cost when the team isn't used to managing stateful streaming applications. You need to consider: checkpoints, savepoints, parallelism, state backend, backpressure, recovery, state management, scaling, observability and stateful job evolution.

This doesn't mean Flink is necessarily more expensive. It means the operational complexity and programming model must be justified by business needs.

If your application needs extremely low latency, complex per-event processing or an advanced state model, that complexity can be fully justified.

A practical example

Imagine a banking transaction monitoring system.

With Spark you can process events in micro-batches:

Kafka
  │
  ▼
Spark Structured Streaming
  │
  ├── micro-batch
  ├── transformation
  ├── aggregation
  └── detection
  │
  ▼
Result

If the business accepts latency in the hundreds of milliseconds or seconds range, Spark can be perfectly sufficient.

Now imagine you need: detect complex patterns, maintain per-customer state, react to individual events, use timers, handle out-of-order events and react immediately to specific sequences. In that scenario, Flink can offer a more natural model.

The question isn't "Is Flink faster than Spark?" The right question is: Is the additional latency and complexity introduced by Spark a real problem for my business?

If the answer is no, switching engines can add complexity without proportional value.

When Kafka Streams is enough

Kafka Streams doesn't need a separate processing cluster. It's a library embedded in an application, typically Java, that connects directly to Kafka.

Kafka Topic
    │
    ▼
Kafka Streams Application
    │
    ├── Filter
    ├── Map
    ├── Join
    ├── Aggregate
    └── State Store
    │
    ▼
Kafka Topic

This is especially attractive when the entire architecture is already built around Kafka. Kafka Streams uses Kafka's partitioning model for horizontal scaling and offers local state stores for stateful operations like aggregations and joins. It also provides exactly-once through Kafka's transactional capabilities.

Where does Kafka Streams shine?

Kafka Streams is an excellent alternative for: event enrichment, filters, transformations, aggregations, topic joins, event processing, Java-centric Kafka applications and event-driven microservices.

Topic customers
      │
      ├──────────┐
      │          │
      ▼          ▼
   Kafka Streams
      │
      ├── Join
      ├── Aggregate
      └── Enrichment
      │
      ▼
Topic result

For this type of scenario, spinning up a Spark or Flink cluster can be unnecessary.

What about historical processing?

Here's an important difference. Kafka Streams is not a general-purpose batch engine like Spark. That doesn't mean it can't reprocess historical data — if historical events are still available in Kafka, it can reprocess them from earlier offsets.

The problem arises when you need to: process terabytes of historical data, run backfills, query multiple sources, perform complex batch transformations, process Data Lake files or run massive SQL processes. For those cases, Spark is a much more natural fit.

Another consideration is language. Kafka Streams is heavily Java/Scala-oriented, while Spark supports Python, Scala, Java, R and SQL. For a PySpark-focused team, this difference can be significant.

Quick decision matrix

Use this guide to decide in 30 seconds:

The human factor

There's one variable that doesn't appear in benchmarks: your team.

If your team writes PySpark every day and knows how to develop, optimize and debug Spark jobs, introducing Flink means adopting a new mental model. It's not just learning another API. It also means understanding state management, checkpoints, savepoints, backpressure, event-time, watermarks, parallelism, recovery, observability and operating stateful applications.

On the other hand, if your team already has strong experience with distributed systems and streaming, Flink can be an excellent investment when the business truly needs those capabilities.

And if your entire stack is Kafka and your team mainly develops Java applications, Kafka Streams can significantly reduce operational friction.

That's why my practical recommendation is: Spark by default, Flink by necessity, Kafka Streams by proximity to Kafka. Not because one is universally better, but because each technology optimizes for a different problem.

How to make the decision

A simple way to decide is to start with the need, not the technology.

Do you need streaming?
        │
        ▼
Do you already have Spark?
        │
   ┌────┴────┐
   │         │
  Yes       No
   │         │
   ▼         ▼
Very low    Is your
latency?  architecture
        Kafka-centric?
   │         │
 ┌─┴─┐     ┌─┴─┐
No   Yes   Yes  No
 │    │     │    │
 ▼    ▼     ▼    ▼
Spark Flink Kafka Evaluate
                 Spark/Flink

Existing architecture should weigh as much as latency requirements.

If you already have CDE + Spark + Airflow + Iceberg + Hive + Data Lake, adopting Spark Structured Streaming is operationally much simpler.

If you have Kafka + Topics + Schema Registry + Microservices + Java, Kafka Streams is a natural choice.

If you have Kafka + Flink + Keyed State + Timers + Event Time + Complex Events, Flink has a clear advantage.

Cloudera Streaming: the integrated platform

If you work in the Cloudera ecosystem, you don't choose streaming engines in isolation. Cloudera packages them as an integrated platform called Cloudera Streaming, bringing Apache Kafka and Apache Flink under a unified operational and governance umbrella.

The main components are:

Cloudera Streaming

Cloudera Streams Messaging
  └── Apache Kafka (resilient transport)
  └── Schema Registry (versioned schemas)
  └── Surveyor (observability on K8s)

Cloudera Streaming Analytics
  └── Apache Flink (stateful processing)
  └── SQL Stream Builder (Flink SQL via UI)

Cloudera Data Flow
  └── NiFi (secure ingestion)
  └── Kafka (transit)
  └── Flink (real-time processing)

Cloudera SDX
  └── Unified governance: access, lineage, audit

Cloudera Streams Messaging provides Kafka as the enterprise messaging backbone, with Schema Registry for centralized, versioned schema management and Surveyor for Kubernetes observability. It's the transport layer that guarantees durability and resilience.

Cloudera Streaming Analytics is the processing layer built on Apache Flink, and includes SQL Stream Builder, an interface that lets analysts and data scientists build and manage Flink SQL jobs without writing Java code. This broadens the range of users who can create streaming pipelines.

Cloudera Data Flow (CFM) completes the cycle by combining NiFi for secure ingestion, Kafka for transit and Flink for real-time processing. All of this runs under Cloudera SDX, which unifies governance, access, lineage and audit across the entire platform.

This changes the decision. In Cloudera, you don't choose between Spark and Flink as if they were mutually exclusive: you have CDE for Spark (unified batch + streaming) and Cloudera Streaming for Kafka + Flink (stateful processing, low latency). Both coexist on the same platform, under the same governance model.

The decision isn't just technical

One of the most common mistakes is picking an engine purely by benchmark. For example: "Flink has lower latency, so we must use Flink." Not necessarily. If your business needs 500 ms latency and Spark can deliver 100 ms, then a Flink architecture might be solving a problem that doesn't exist.

Similarly: "We already have Spark, so everything must be done with Spark." Not necessarily either. If you need complex stateful event processing, timers and very low latency, sticking with Spark just for standardization can lead to a suboptimal architecture.

The decision should consider:

             ┌───────────────┐
             │   Business    │
             └───────┬───────┘
                     │
          ┌──────────┼──────────┐
          ▼          ▼          ▼
       Latency     State     Volume
          │          │          │
          └──────────┼──────────┘
                     ▼
              ┌─────────────┐
              │ Architecture│
              └──────┬──────┘
                     │
          ┌──────────┼──────────┐
          ▼          ▼          ▼
        Spark       Flink    Kafka Streams

Conclusion

All three engines excel at what they do.

Spark Structured Streaming provides excellent batch and streaming integration and is especially attractive when you already have a Spark ecosystem, Data Lake and processes built with DataFrames, SQL or PySpark.

Apache Flink offers a particularly powerful model for stateful streaming, event-time, windows and complex per-event processing. It's especially interesting when latency and state complexity are real business requirements.

Kafka Streams offers simplicity and proximity to Kafka. When the application is essentially a microservice that consumes events from Kafka, processes them and publishes back to Kafka, adding a distributed processing engine can be unnecessary.

The question isn't which is the best streaming engine? The question is which is the best engine for my stack, my team and my latency?

Answer that first, and the right technology picks itself much more easily.

And there's one final consideration that's often forgotten: the best architecture isn't necessarily the one with the lowest latency. It's the one that meets business requirements with the lowest reasonable operational complexity.

Your streaming engine is one piece. Transformation is the full board.

— Jorge. Data Engineer at Accenture, working with streaming on Cloudera CDP and Databricks.

Sources

• Apache Spark — Structured Streaming Programming Guide: spark.apache.org/docs/latest/streaming/index.html
• Apache Flink — Stateful Stream Processing: nightlies.apache.org/flink/flink-docs-stable/docs/concepts/stateful-stream-processing/
• Apache Flink — Fault Tolerance and Checkpointing: nightlies.apache.org/flink/flink-docs-stable
• Apache Kafka — Kafka Streams Documentation: kafka.apache.org/documentation/streams
• Apache Kafka — Kafka Streams Core Concepts: kafka.apache.org/28/documentation/streams/core-concepts
• Databricks — Structured Streaming Documentation: databricks.com/product/structured-streaming
• Cloudera — Streaming Platform Overview: cloudera.com/products/stream-processing.html
• Cloudera — Cloudera Streaming Analytics: cloudera.com/products/stream-processing/streaming-analytics.html
• Cloudera — Cloudera Streams Messaging: cloudera.com/products/stream-processing/streams-messaging.html
• Cloudera — SQL Stream Builder: cloudera.com/products/stream-processing/sql-stream-builder.html