Learn Apache Kafka Online | Comprehensive Kafka Course & Training
- 1. Introduction to Apache Kafka & Event Streaming
- 2. What is Apache Kafka? Apache Kafka is an open-source distributed event streaming platform. Originally developed by LinkedIn, now part of Apache Software Foundation. Designed for real-time data processing, messaging, and event-driven architectures. Handles high-throughput, low-latency, and fault-tolerant data streaming.
- 3. Why Use Apache Kafka? ā Scalability ā Handles millions of events per second. ā Durability ā Stores events for long-term retrieval. ā Fault Tolerance ā Replicates data across multiple nodes. ā High Performance ā Processes real-time and batch data efficiently. ā Decouples Services ā Enables microservices and event-driven architecture.
- 4. Kafka Architecture Overview Producers ā Publish events/messages to Kafka topics. Topics ā Logical categories where events are stored. Partitions ā Distributes data across multiple brokers for scalability. Brokers ā Kafka servers that manage message storage and retrieval. Consumers ā Subscribe to topics and process messages. ZooKeeper ā Manages Kafka metadata and broker coordination.
- 5. Key Components of Apache Kafka š¹ Producers ā Send data to Kafka topics. š¹ Topics & Partitions ā Store and organize messages. š¹ Brokers ā Handle storage, replication, and retrieval. š¹ Consumers ā Subscribe and process events. š¹ ZooKeeper ā Coordinates brokers and manages leader elections.
- 6. Event Streaming with Kafka Kafka enables real-time event streaming across applications. Event Producers generate data continuously (e.g., IoT sensors, logs, transactions). Kafka Streams API allows real-time processing of streaming data. Event Consumers process and act upon streamed data (e.g., analytics, monitoring).
- 7. Use Cases of Kafka ā Real-Time Analytics ā Process and analyze live data. ā Log & Metrics Aggregation ā Centralize logs for monitoring. ā Fraud Detection ā Identify suspicious transactions in real-time. ā IoT & Sensor Data Processing ā Stream data from connected devices. ā Messaging & Microservices ā Enables scalable, decoupled architectures.
- 8. Feature Throughput Storage Scalability Processing Model Apache Kafka High (millions of events/sec) Retains messages for days/weeks Horizontally scalable Pub-Sub & Event Streaming Traditional Messaging (e.g., RabbitMQ) Moderate Messages are deleted after consumption Limited scalability Queue-based Kafka vs. Traditional Messaging Systems
- 9. Conclusion Apache Kafka is a powerful event streaming platform for handling real- time data. Provides high throughput, fault tolerance, and scalability. Ideal for big data pipelines, microservices, and analytics.