What is Kafka?
Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming applications. It was originally developed by LinkedIn and later open-sourced as part of the Apache Software Foundation. Kafka is designed to handle large volumes of data streams in a fault-tolerant and scalable manner.
Key features of Apache Kafka include:
- Publish-Subscribe Model: Kafka follows a publish-subscribe model where producers publish data to topics, and consumers subscribe to these topics to receive the data. This enables decoupling between data producers and consumers.
- Distributed and Fault-Tolerant: Kafka is designed to operate in a distributed and fault-tolerant manner. It can scale horizontally by adding more broker nodes to the Kafka cluster, and it replicates data across multiple nodes to ensure resilience against node failures.
- High Throughput and Low Latency: Kafka is optimized for high throughput and low-latency data streaming. It can handle a large number of events per second, making it suitable for real-time data processing.
- Durability: Kafka persists data to disk, ensuring durability even in the case of node failures. This durability is achieved by replicating data across multiple broker nodes.
- Retention of Data: Kafka allows configurable retention periods for data, meaning that data can be stored for a specified duration, and consumers can go back in time to retrieve historical data.
Why Kafka is Required:
- Real-Time Data Streaming: Kafka is designed for handling real-time data streaming, making it suitable for scenarios where data needs to be processed and analyzed as it arrives.
- Decoupling of Systems: Kafka facilitates the decoupling of data producers and consumers. Producers can publish data to Kafka topics without worrying about who consumes the data, and consumers can subscribe to relevant topics without needing to know the producers.
- Scalability: Kafka is horizontally scalable, meaning that it can handle increasing data loads by adding more broker nodes to the cluster. This scalability is essential for growing data requirements.
- Fault Tolerance: Kafka’s distributed and replicated architecture ensures fault tolerance. Even if some nodes fail, data is still available from replicated copies on other nodes.
- Event Sourcing and Change Data Capture: Kafka is used in event sourcing architectures, where changes to the state of an application are captured as events. It’s also employed in change data capture scenarios where changes in databases are captured and streamed in real time.
- Log Aggregation: Kafka is often used for log aggregation, allowing systems to publish logs to Kafka topics, making them available for centralized processing and analysis.
- Integration in Microservices Architectures: Kafka serves as a communication layer between microservices in distributed systems, enabling them to exchange events and messages.
Overall, Kafka is a versatile tool for managing the flow of data in real-time, providing a robust and scalable solution for building streaming data architectures and event-driven applications.