The Difference Between Data Streams and Stream Processing
Kafka vs Flink sounds like the title fight between two Eastern European boxers, but are in actuality far more like Rocky and Apollo working together to take down Ivan Drago. Kafka and Flink are two of the most powerful tools in the modern data infrastructure stack — often mentioned together, but serving very different purposes. Both are used for handling streaming data, but if you’re trying to decide between them (or how to use them together), it’s critical to understand what each actually does under the hood.
At a high level: Kafka moves data, and Flink processes it. But that distinction hides a lot of nuance — about architecture, guarantees, scaling, and how each fits into the data ecosystem.
Apache Kafka: The Distributed Commit Log
Kafka is a distributed event streaming platform — a durable, high-throughput system for ingesting, storing, and delivering streams of records. Think of it as a massively scalable message bus or a distributed commit log. Producers write events to topics; consumers read those events independently and at their own pace.
Kafka’s real magic lies in its persistence and ordering guarantees. Every message is stored on disk in append-only logs, partitioned across brokers. Consumers maintain their own offsets, which means Kafka can serve as both a real-time event broker and a replayable data store.
In practice, Kafka is used for:
- Collecting telemetry or clickstream data from applications
- Decoupling microservices through event-driven architectures
- Serving as a buffer between operational and analytical systems
- Powering pub/sub pipelines that feed downstream processors like Flink, Spark, or ksqlDB
Kafka guarantees durability and fault-tolerance through replication. It can handle millions of events per second, scale horizontally, and preserve message ordering within partitions. But what Kafka doesn’t do is complex computation. It’s not built for aggregations, joins, or stateful transformations — at least not natively. That’s where Flink enters the picture.
Apache Flink: The Stateful Stream Processor
If Kafka is the bloodstream, Flink is the brain. Apache Flink is a stateful stream processing framework designed to compute over unbounded (infinite) data streams in real time.
Flink excels at event-time processing, windowing, joins, aggregations, and state management. It ingests data from sources like Kafka, transforms it in real time, and outputs it to sinks such as databases, data lakes, or dashboards. Its architecture allows it to handle millions of events with sub-second latency while maintaining exactly-once semantics — something notoriously difficult in distributed systems.
A typical Flink pipeline might look like this:
- Kafka produces messages to a topic.
- Flink reads those messages as a stream source.
- It aggregates, filters, or enriches the data.
- Results are written to a sink (Elasticsearch, PostgreSQL, S3, etc.).
Flink’s key strength lies in its stateful computations. It can track ongoing counts, maintain session information, and compute running aggregates across massive event streams. Its internal state backend (RocksDB or in-memory) allows efficient recovery and fault tolerance through checkpointing.
Where Kafka stores and replays data, Flink interprets and transforms it — making it the engine that turns streams into insights.
Kafka vs Flink – Islands in the Streams
A common point of confusion arises with Kafka Streams, Kafka’s own lightweight processing library. Kafka Streams allows developers to build processing logic directly within their Kafka consumer apps. It’s great for simple aggregations, filtering, and joins.
However, Flink is far more powerful when:
- You need event-time rather than processing-time semantics
- You require complex, stateful operations
- You’re dealing with multiple input sources or non-Kafka data
- You need to scale independently of Kafka brokers
Kafka Streams is embedded and simple; Flink is distributed and robust. In large-scale, low-latency architectures, Flink often takes over when Kafka Streams hits its operational ceiling.
Architecture and Deployment
Kafka runs as a cluster of brokers and ZooKeeper (or KRaft) nodes. Producers and consumers connect via TCP. It’s designed for storage and transport, not computation.
Flink, by contrast, runs as a JobManager and multiple TaskManagers, with parallelism across nodes. Jobs are submitted via a REST or CLI interface, and execution is distributed across TaskManagers.
For production systems, Kafka often sits at the center of a data architecture, feeding multiple downstream processors. Flink runs beside it, continuously consuming and transforming streams into analytical results or materialized views.
When to Use Which
| Use Case | Choose Kafka | Choose Flink |
|---|---|---|
| Event transport and buffering | ✅ | |
| Durable message storage | ✅ | |
| Simple stream filtering or routing | ✅ (Kafka Streams) | |
| Stateful aggregations or joins | ✅ | |
| Complex event-time processing | ✅ | |
| Streaming ETL and analytics | ✅ | |
| Real-time dashboards | ✅ | |
| Decoupling microservices | ✅ |
Most real-world data platforms use both: Kafka for event delivery, Flink for computation. Kafka feeds streams into Flink jobs, and Flink outputs enriched data back into Kafka or external systems.
Kafka vs Flink is Misleading
Kafka and Flink aren’t competitors; they’re complements. Kafka provides the durable backbone for moving data between systems. Flink adds the computational layer that gives that data meaning in motion.
If you think in systems terms: Kafka is like the network layer — moving packets efficiently and reliably — while Flink is the application layer, interpreting and transforming those packets into real-time intelligence.
For developers, the right mindset isn’t “Kafka vs Flink” but “Kafka and Flink.” Together, they’re the foundation of modern real-time data architectures — scalable, resilient, and built for a world where data never stops moving.
Kafka vs Flink – a Technical Comparison
| Category | Apache Kafka | Apache Flink |
|---|---|---|
| Primary Purpose | Distributed event streaming platform for publishing, storing, and delivering data streams. | Stateful stream processing engine for computing and analyzing data streams in real time. |
| Core Functionality | Message transport, persistence, and replay with partitioned logs. | Event-time computation, windowing, aggregation, and complex stream transformations. |
| Data Model | Log-based event streams organized into topics and partitions. | Continuous, unbounded data streams represented as DataStreams or Tables. |
| Architecture | Broker-based cluster with Producers, Consumers, and Topics. Uses ZooKeeper or KRaft for coordination. | Master–worker model with JobManager (control plane) and TaskManagers (execution). |
| Programming Model | Producer/Consumer API, Kafka Streams API, Connect API. | APIs for DataStream, DataSet (batch), Table, and SQL. Supports event-time semantics. |
| Processing Mode | Primarily at-least-once (exactly-once possible with Streams API). | Exactly-once processing with checkpointing and state backends. |
| State Management | Stateless (brokers only store messages). Application state handled externally. | Built-in stateful computation with RocksDB or in-memory state backend. |
| Fault Tolerance | Replication across brokers, durable logs, consumer offset recovery. | Checkpointing, state snapshots, and recovery through distributed coordination. |
| Scalability | Horizontally scalable through topic partitions and consumer groups. | Scales by parallelizing tasks across TaskManagers and slots. |
| Latency | Low (milliseconds to seconds), depends on consumer processing. | Sub-second latency for event processing, depending on job complexity. |
| Throughput | Extremely high; can handle millions of messages per second. | High, but dependent on computation complexity and state size. |
| Data Retention | Configurable (time-based or size-based retention). Historical replay supported. | No built-in retention (relies on upstream systems like Kafka for replay). |
| Ordering Guarantees | Per-partition ordering guaranteed. | Can preserve order per key when using keyed streams. |
| Deployment Model | Runs as a distributed cluster of brokers; can be on-prem or cloud-managed (Confluent Cloud, MSK). | Runs as a distributed job cluster; can integrate with Kubernetes, YARN, or standalone setups. |
| Integration Ecosystem | Integrates with Flink, Spark, ksqlDB, Debezium, and data warehouses. | Integrates with Kafka, Pulsar, Kinesis, and external sinks (JDBC, Elasticsearch, S3). |
| Typical Use Cases | Event streaming, pub/sub, messaging backbone, log aggregation, data transport. | Real-time analytics, ETL, event-driven applications, anomaly detection, complex event processing. |
| Language Support | Java, Scala, Python, Go, REST. | Java, Scala, Python, SQL. |
| Data Sources / Sinks | Primarily Kafka topics; Connect API enables external connectors. | Multiple connectors for Kafka, JDBC, Filesystems, Object Stores, REST APIs. |
| Event Time Handling | Basic timestamping and ordering; limited event-time semantics. | Advanced event-time handling, late event processing, and watermarking. |
| Ease of Use | Easier setup; configuration-driven. Limited computation model. | Steeper learning curve; requires understanding of distributed stream semantics. |
| Managed Services | Confluent Cloud, AWS MSK, Azure Event Hubs for Kafka. | Offered via Ververica Platform, Amazon Kinesis Data Analytics, and other managed Flink services. |
| License | Apache 2.0 (fully open source). | Apache 2.0 (fully open source). |
| Best For | Reliable data transport and buffering between systems. | Real-time data processing, enrichment, and analytics. |
