Buying snapshot
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| Decision point | What the project states |
|---|---|
License and governance | Open source, developed as a project of the Apache Software Foundation. |
Installation | Download a release or build one with the Gradle wrapper, format storage with a cluster ID, then start the broker. An apache/kafka Docker image is also available. |
Java requirement | Java must be installed. The project builds and tests with Java 17 and 25; client and Streams modules target Java 11, the rest Java 17. |
Integration | The Connect interface links hundreds of event sources and sinks, including Postgres, JMS, Elasticsearch and AWS S3. Client libraries read, write and process streams in many programming languages. |
Our take
Choose Apache Kafka when many systems must exchange high-volume event streams and you want durable storage, stream processing and connectors in one open-source platform, with engineers who will own a distributed Java cluster.
Evaluate Apache Kafka
Popular comparisons
See all 7 Apache Kafka comparisons
Where it stands out
Scale with durable storage. The project says production clusters scale to a thousand brokers, trillions of messages per day, petabytes of data and hundreds of thousands of partitions. Streams are stored in a distributed, durable, fault-tolerant cluster, and storage and processing can expand and contract elastically.
Processing built in. Built-in stream processing handles joins, aggregations, filters and transformations, using event-time processing.
Guarantees for critical flows. The project lists guaranteed ordering, zero message loss and exactly-once processing for mission-critical use cases. Clusters can stretch across availability zones or connect across geographic regions.
Product screenshot
Trade-offs to weigh
Operations sit with your team. Kafka is software you deploy yourself, so broker sizing, storage and availability across zones or regions are your engineers' work.
Performance depends on your cluster and network. The project describes throughput as network-limited across a cluster of machines, with latencies as low as 2 ms, so hardware and network design shape the results you get.
Scala 2.13 only. Scala 2.13 is the only supported Scala version, which matters if your own builds depend on Kafka's Scala modules.
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
604 GitHub commits 90d34.2k GitHub stars0 vulnerabilities across 2 packagesOpenSSF score 5.9/10
See all signals from 9 sources
PyPI · confluent-kafka@2.16.1 · npm · kafkajs@2.2.4
Alternatives to Apache Kafka
The reviewed substitutes for Apache Kafka among the event streaming platforms, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Confluent
- Two event streaming platforms carrying ordered, partitioned, replayable data for the same pipelines. They are compared directly on protocol, operations and commercial support, and a team runs one as its event backbone.Applies to: Choosing the event streaming platform that will carry the organisation's event data.
- Redpanda
- Choose Redpanda if you want Kafka compatibility with dramatically simpler operations and low latency.Applies to: Choosing between two products of the same kind for one job.
- Apache Pulsar
- Choose Apache Pulsar if you need multi-tenant messaging with geo-replication and want the flexibility to scale compute and storage independently.Applies to: Choosing the event streaming platform that will carry the organisation's event data.
- AWS Kinesis
- Choose AWS Kinesis if you run on AWS, want zero operational overhead, and your throughput requirements fit within the shard-based scaling model.Applies to: Choosing between two products of the same kind for one job.
- Azure Event Hubs
- An event backbone that ingests large streams of data from apps and devices can be built on Apache Kafka or on Azure Event Hubs. Event Hubs is Microsoft's fully managed service on Azure and accepts clients written for the Kafka protocol. Kafka is open source and portable across clouds and data centers, with a large connector and stream-processing ecosystem around it.Applies to: Choosing between these two for the event streaming decision.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- RabbitMQ
- Choose RabbitMQ if your primary need is traditional message queuing with complex routing logic rather than high-throughput event streaming.Applies to: When services exchange messages at a volume that either a queue or a replayable log can carry.
- Redis
- Redis can carry queues and streams alongside its main role as an in-memory store, so teams with Redis already running ask whether it removes the need for a dedicated broker or streaming platform. The answer depends on durability, replay and throughput requirements, which makes it an architecture decision rather than a like-for-like comparison.Applies to: Whether an in-memory store already in the stack can carry messaging, or a dedicated broker is needed.
Related technologies
Normally used together rather than chosen between, so these are not alternatives.
- Apache Flink
- A streaming platform stores and delivers ordered events; a processing engine computes over them. The documented architecture runs the engine against the platform's topics, so the pair is a transport-and-compute pairing rather than a substitution.Applies to: Whether an event streaming platform can process events itself, or needs a compute engine.
- Apache NiFi
- The two sit at different layers of one system and the documented deployments run them together, so the reader's question is which job each one does rather than which to buy.Applies to: Whether these two do the same job, or different jobs in one pipeline.
- Apache Spark
- A streaming platform stores and delivers ordered events; a processing engine computes over them. The documented architecture runs the engine against the platform's topics, so the pair is a transport-and-compute pairing rather than a substitution.Applies to: Whether an event streaming platform can process events itself, or needs a compute engine.