Apache Kafka has become the de facto standard for building real-time data pipelines and streaming applications. As a distributed event streaming platform, it has revolutionized how companies handle data flows. Let's dive into what makes Kafka tick. Key Concepts: 1. Topics: • Think of these as categories or feed names • Messages are published to topics • Can have multiple partitions for parallelism 2. Partitions: • Ordered, immutable sequence of records • Each record assigned a sequential ID (offset) • Enables massive scalability 3. Producers: • Publish messages to topics • Can choose which partition to send messages to 4. Consumers: • Subscribe to topics and process the messages • Track their position (offset) in each partition 5. Brokers: • Kafka servers that store and manage topics • A cluster typically has multiple brokers for fault tolerance 6. KRaft: • Manages the Kafka cluster • Tracks broker health, topic configuration, and more How It All Connects: 1. Message Flow: Producer → Broker → Consumer 2. Partition Leadership: • Each partition has one leader broker and multiple replicas • Writes go to the leader, replicas stay in sync 3. Consumer Groups: • Multiple consumers can work together • Each partition is read by only one consumer in a group 4. Offset Management: • Consumers commit their offset after processing • Enables restart from last position if a consumer fails 5. Retention: • Messages can be retained for a configured time or size • Enables replay and catch-up scenarios Key Features: • High Throughput: Can handle millions of messages per second • Fault Tolerance: Replication ensures data safety • Scalability: Easy to scale out by adding more brokers • Low Latency: Sub-10 ms latency in production environments • Durability: Data persisted to disk, surviving broker failures Use Cases: • Event Sourcing • Log Aggregation • Stream Processing • Metrics Collection • Activity Tracking Kafka's architecture enables decoupling of data streams and systems. This makes it invaluable for building real-time data pipelines and streaming applications. Pro Tip: When designing Kafka-based systems, carefully consider your partitioning strategy. It's crucial for performance and scalability. Credit: Brij kishore Pandey
