Apache Kafka overview

Apache Kafka is a distributed event streaming platform designed to handle high volume real-time data feeds. It’s highly scalable, durable, and fault-tolerant. Here’s a brief overview of its architecture and components:

Brokers

A Kafka cluster consists of one or more servers known as brokers, which manage the storage and transportation of messages. Each broker can handle terabytes of messages without performance impact.

Topics

Topics are the primary unit of data in Kafka. They’re similar to tables in a database and are used to categorize data. Producers write data to topics and consumers read from them.

Partitions

Each topic in Kafka is split into one or more partitions. Partitions allow for data to be parallelized across the Kafka cluster, enabling greater scalability.

Producers and Consumers

Producers publish data to Kafka topics, while consumers read this data. Kafka ensures that data within each partition is consumed in the order it was produced⁹.

Scalability

Kafka is highly scalable, both horizontally (adding more machines) and vertically (adding more power to existing machines), accommodating growing data needs without sacrificing performance.

High Availability

Kafka guarantees high availability through features like replication and partitioning. It can recover quickly from failures, ensuring reads and writes are always available.

Security

Kafka supports features like authentication, authorization, and encryption to ensure data security. It also provides audit logs to track activities.

Kafka Streams

Kafka Streams is a client library for building applications and microservices where the input and output data are stored in Kafka clusters. It simplifies application development by leveraging Kafka’s native capabilities¹. It is very much like RXJS's Observables, based on the same stream based operational approach to handle data changes over time.

Kafka Connect and Kafka ksqlDB

Kafka Connect is a tool for streaming data between Kafka and other data systems. Kafka ksqlDB, on the other hand, is a database purpose-built for stream processing applications, allowing you to build real-time systems on a SQL-like interface.

Caching, Event-Driven Architecture, Event Sourcing, and Sharding

Kafka supports caching for faster data retrieval. Its event-driven architecture ensures that actions are triggered by events. Event sourcing is a technique where changes to application state are stored as a sequence of events. Sharding, a type of database partitioning, is also used in Kafka for distributing data across different databases or servers.

In conclusion, Apache Kafka is a robust and versatile platform that can handle real-time data streaming at a large scale. Its architecture and components work together to ensure high performance, scalability, and reliability.