Apache Kafka is a distributed event streaming platform capable of handling trillions of events per day. Originally developed at LinkedIn, Kafka is now used by thousands of companies for real-time data pipelines, streaming analytics, and event-driven architectures.
Core Concepts
Topics
Data streams are organized into topics—named feeds of messages:
- Producers write to topics
- Consumers read from topics
- Topics can have multiple partitions for parallelism
Partitions
Topics are split into partitions for scalability:
- Each partition is an ordered, immutable sequence of records
- Consumers can read partitions in parallel
- Partition count determines parallelism
Consumer Groups
Consumers form groups to divide work:
- Each partition is consumed by exactly one consumer in the group
- Allows horizontal scaling of consumers
- Multiple groups can read the same topic independently
Kafka Architecture
``
Producers → [Topic: orders] → Consumers
│
┌─────┴─────┐
│ Partition │
│ 0 │
├───────────┤
│ Partition │
│ 1 │
├───────────┤
│ Partition │
│ 2 │
└───────────┘
Key Features
- High Throughput: Millions of messages per second
- Low Latency: Sub-millisecond message delivery
- Durability: Data replicated across brokers
- Scalability: Add brokers without downtime
- Fault Tolerance: Automatic leader election
Kafka Ecosystem
- Kafka Connect: Pre-built connectors to data sources/sinks
- Kafka Streams: Stream processing library
- ksqlDB: SQL interface for stream processing
- Schema Registry: Manage message schemas
Kafka Use Cases
1. Event Sourcing: Store all state changes as events
2. Change Data Capture (CDC): Capture database changes
3. Microservices Communication: Async event-driven messaging
4. Real-time Analytics: Process events as they arrive
5. Log Aggregation: Collect logs from distributed systems