What is Apache Kafka?
Apache Kafka is a distributed event streaming platform capable of handling trillions of events per day
Introduction to Apache Kafka
Apache Kafka is an open-source distributed event streaming platform originally developed at LinkedIn in 2010 and later donated to the Apache Software Foundation in 2011. It is designed for high-throughput, fault-tolerant, and scalable real-time data streaming.
The name "Kafka" was chosen because the system is optimized for writing, and the creator (Jay Kreps) liked the author Franz Kafka. Today, Kafka is used by over 80% of Fortune 500 companies for mission-critical applications.
Distributed
Runs as a cluster across multiple servers, data centers, or cloud regions
Durable
Persists streams of records safely with configurable retention
Scalable
Handles millions of events/second with horizontal scaling
Kafka History & Evolution
2010
Developed at LinkedIn to handle their massive data pipeline needs (activity tracking, metrics, logs)
2011
Open-sourced and donated to Apache Software Foundation
2014
Confluent founded by Kafka creators to commercialize and advance Kafka
2016
Kafka Streams API released - enabling stream processing within Kafka
2022+
KRaft (Kafka Raft) removes ZooKeeper dependency, simplifying deployment
The Three Core Capabilities
Kafka combines three key capabilities that are usually handled by separate systems:
Publish & Subscribe (Messaging)
Like a message queue, but with multiple subscribers. Producers publish messages to topics, multiple consumer groups can read independently.
Store (Durable Storage)
Unlike traditional message queues, Kafka persists messages durably. Data can be retained for days, weeks, or forever. Replay messages from any point.
Process (Stream Processing)
Process streams of data in real-time with Kafka Streams API or ksqlDB. Transform, aggregate, join, and analyze data as it flows through.
Real-World Use Cases
Messaging
Replace traditional message brokers (RabbitMQ, ActiveMQ) for high-throughput, low-latency messaging between microservices.
Activity Tracking
Track user activity like page views, clicks, searches into topics for real-time analytics and recommendations.
Log Aggregation
Collect logs from multiple services into a central location for monitoring, alerting, and analysis (ELK stack integration).
Stream Processing
Real-time processing pipelines for data transformation, enrichment, and aggregation as data flows through.
Event Sourcing
Store immutable sequence of events as the source of truth. Rebuild application state by replaying events.
Commit Log
External commit log for distributed systems. Database change data capture (CDC) and cross-datacenter replication.
Who Uses Kafka?
Kafka powers mission-critical systems at the world's largest companies:
7+ trillion messages/day
Netflix
Real-time streaming analytics
Uber
Trillions of events/day
Airbnb
Event-driven architecture
Spotify
Log aggregation & analytics
Real-time data pipelines
Goldman Sachs
Financial transactions
PayPal
Fraud detection
Kafka vs Traditional Message Queues
Understanding how Kafka differs from traditional message queues like RabbitMQ or ActiveMQ:
| Feature | Kafka | Traditional MQ |
|---|---|---|
| Message Retention | Configurable (hours/days/forever) | Until consumed (deleted after) |
| Throughput | Millions/sec | Thousands/sec |
| Message Replay | Yes, by offset | No |
| Consumer Model | Pull-based (consumer controls) | Push-based (broker controls) |
| Ordering | Per partition | Per queue |
| Multiple Consumers | Multiple groups read independently | Competing consumers (one gets it) |
| Best For | High-volume event streams | Task queues, RPC |