Kafka

Event streaming platform
Real-time data processing
Scalable message broker
Fault-tolerant storage
Pub-sub & message queuing
High throughput, low latency

Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant, and real-time data processing. It enables applications to publish, subscribe to, store, and process event streams at scale. Kafka is widely used for log aggregation, real-time analytics, messaging, and microservices communication. It operates using a producer-consumer model, with data stored in durable, partitioned logs for efficient processing. Kafka’s high availability, scalability, and durability make it ideal for event-driven architectures, IoT data streaming, and big data applications. It integrates with Apache Spark, Flink, and Elasticsearch, supporting complex real-time data workflows.

FAQs

What is Apache Kafka used for?
Kafka is used for real-time data streaming, log processing, event-driven architectures, and messaging in large-scale applications.
How does Kafka ensure fault tolerance?
Kafka replicates data across multiple brokers, ensuring fault tolerance and data durability, even if a node fails.
What is the difference between Kafka and traditional message queues?
Unlike traditional message queues, Kafka persists data for a configurable time, allowing multiple consumers to read messages independently.
Can Kafka handle large-scale data processing?
Yes, Kafka is designed for high-throughput and low-latency processing, making it suitable for big data and IoT applications.
How does Kafka handle scalability?
Kafka scales horizontally by adding more brokers and partitions, allowing it to handle millions of messages per second efficiently.