@high_tech02: 🔹 What is Kafka? Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant, and scalable data pipelines. 🔹 Core Concepts: 1. Topics: Categories for streaming data 2. Partitions: Distributed, ordered logs within topics 3. Producers: Applications sending data to Kafka 4. Consumers: Applications reading data from Kafka 5. Brokers: Servers hosting topics and partitions 6. Consumer Groups: Scalable, fault-tolerant consumer clusters 🔹 Key APIs: • Producer API: Publish streams • Consumer API: Subscribe to topics • Streams API: Stream processing • Connect API: Build/run reusable producers/consumers 🔹 Advanced Features: • Exactly-once semantics • Transactional writes • Idempotent producers • KRaft (Kafka Raft): Replacing ZooKeeper for metadata management 🔹 When to Use Kafka: • Real-time data pipelines • Activity tracking • Metrics collection • Log aggregation • Stream processing • Event-sourcing • Commit log service 🔹 Alternatives: • Apache Pulsar: Multi-tenancy, geo-replication. Use when: need tiered storage, multiple namespaces • RabbitMQ: Complex routing, low latency. Use when: priority queues, request-reply patterns crucial • Amazon Kinesis: Fully managed. Use when: AWS ecosystem integration paramount • Google Pub/Sub: Global message bus. Use when: multi-region, exactly-once semantics required 🔹 Kafka shines in: • Large-scale data pipelines • Microservices event backbone • Real-time analytics and ML feature stores 🔹 When to reconsider: • Small-scale applications (overhead may outweigh benefits) • Strict ordering requirements across all messages • Need for complex message routing What's your take on Kafka vs alternatives in production environments? Any performance insights to share?

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Saturday 03 August 2024 13:01:33 GMT