What is
Kafka
?
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At a glance
Attribute | Value |
|---|---|
Primary use case | Distributed event streaming for real-time data pipelines, analytics, and integration |
Deployment type | Self-hosted clusters or third-party managed cloud services |
Pricing model | Free/open source; infrastructure, operations, and third-party managed services incur separate costs |
API compatibility | Kafka protocol; Producer, Consumer, Streams, Connect, and Admin APIs |
Supported integrations | Postgres, JMS, Elasticsearch, AWS S3 |
Source type | Open |
Cloud provider | Cloud Agnostic |
Required skills |
|
AEP branch | Tooling |
AEP category | Resources |
AEP subcategory | Data |
Profile
Data is Kafka’s most specific AEP level. Apache Kafka is an open-source distributed event-streaming platform for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. It stores streams in a distributed, durable, fault-tolerant cluster and supports event processing and integrations through Kafka Connect.
Kafka supports high-throughput messaging, persistent storage, availability across zones or geographic regions, and stream processing. The Apache Kafka website reports latency as low as 2 ms and production clusters scaling up to 1,000 brokers, trillions of messages per day, petabytes of data, and hundreds of thousands of partitions.
Focus
Kafka serves organizations building real-time data pipelines, streaming analytics, data integrations, and mission-critical applications. It provides a platform for capturing, storing, processing, and routing event streams, including processing with joins, aggregations, filters, and transformations.
Background
The supplied research does not specify Kafka’s founding date, founding history, or funding. It identifies Kafka as open source and as one of the Apache Software Foundation’s five most active projects. The Apache Kafka website states that its content is copyright the Apache Software Foundation under the Apache License v2 and reports a community with hundreds of meetups around the world.
Features
High throughput
Kafka delivers messages at network-limited throughput across a cluster of machines, with latency as low as 2 ms.
Scalable clusters
Kafka supports large production workloads and elastic expansion or contraction of storage and processing. The website reports scale up to 1,000 brokers, trillions of messages per day, petabytes of data, and hundreds of thousands of partitions.
Permanent storage
Kafka stores data streams in a distributed, durable, fault-tolerant cluster, providing persistent storage for event data.
High availability
Kafka supports cluster deployment across availability zones and connections between clusters in separate geographic regions.
Built-in stream processing
Kafka supports processing event streams with joins, aggregations, filters, and transformations, using event-time and exactly-once processing.
Connect integrations
Kafka Connect links Kafka with event sources and sinks. The website describes integrations with hundreds of sources and sinks, including Postgres, JMS, Elasticsearch, and AWS S3.
Mission-critical processing
Kafka supports guaranteed ordering, zero message loss, and efficient exactly-once processing for mission-critical use cases.
Pros and cons
Pros
Its distributed, durable, fault-tolerant storage and high availability support resilient event-streaming deployments.
Built-in stream processing handles joins, aggregations, filters, and transformations, including event-time and exactly-once processing.
Kafka Connect integrates with hundreds of event sources and sinks; the project reports use by more than 80% of Fortune 100 companies and more than 5 million unique lifetime downloads.
Cons
Kafka does not prescribe how users should model their data, so teams must define their own event and data-modeling conventions.
Self-hosted use entails separate infrastructure and operational costs; cluster configuration, capacity planning, monitoring, and authentication require dedicated operational skills.
FAQs
Frequently asked questions
How does Kafka process event streams?
Kafka stores event streams in a distributed, durable, fault-tolerant cluster and can process them with joins, aggregations, filters, and transformations.
Which integrations does Kafka support?
Kafka Connect integrates with event sources and sinks including Postgres, JMS, Elasticsearch, and AWS S3.
How much does Kafka cost to run?
Kafka is Free/open source; infrastructure, operations, and third-party managed services incur separate costs.
Can Kafka be self-hosted or managed?
Kafka can run in Self-hosted clusters or third-party managed cloud services.
What skills are needed to operate Kafka?
Operating Kafka requires skills in distributed systems and event-streaming architecture, Kafka cluster and topic configuration, producer and consumer API development, capacity planning and operational monitoring, and authentication and certificate management.
What are Kafka's limitations?
Kafka does not prescribe how users should model their data, so teams must define their own event and data-modeling conventions. Self-hosted Kafka also entails separate infrastructure and operational costs, and requires operational skills for cluster configuration, capacity planning, monitoring, and authentication.
