Kafka

Event streaming is the digital equivalent of the human body’s central nervous system. It is the technological foundation for the 'always-on' world where businesses are increasingly software-defined and automated, and where the user of software is more software.

Technically speaking, event streaming is the practice of capturing data in real-time from event sources like databases, sensors, mobile devices, cloud services, and software applications in the form of streams of events; storing these event streams durably for later retrieval; manipulating, processing, and reacting to the event streams in real-time as well as retrospectively; and routing the event streams to different destination technologies as needed. Event streaming thus ensures a continuous flow and interpretation of data so that the right information is at the right place, at the right time.

Kafka is optimized for high-throughput message handling, enabling the processing of millions of messages per second with low latency. Ideal for scenarios requiring real-time data streaming and processing.

Kafka’s distributed architecture allows for horizontal scaling across multiple nodes, ensuring reliability and fault tolerance. Supports data replication across multiple brokers, maintaining data integrity and availability even in the event of node failures.

Kafka uses a publish-subscribe messaging model, allowing producers to publish data to topics, and consumers to subscribe and process this data in real-time. Supports various consumer models, including real-time streaming and batch processing.

Kafka Streams, an integrated stream processing library, allows users to build complex event-driven applications that process and transform streams of data in real time. Supports stateful processing, windowing, and joins, enabling sophisticated stream processing use cases.

Kafka is designed to scale out by adding more brokers, allowing it to handle increasing volumes of data and traffic with ease. Supports the partitioning of topics, which helps distribute load and scale consumer groups.

Kafka Connect provides a framework for connecting Kafka with external systems, allowing for easy data integration and migration between various data sources and sinks. A wide range of connectors is available for databases, cloud services, and data warehouses.

Kafka stores messages durably on disk and allows for configurable retention policies, ensuring data persistence and the ability to replay messages as needed. Supports log compaction for retaining the latest state of a topic.

Includes security features such as encryption (TLS/SSL), authentication (SASL), and authorization (ACLs), ensuring secure data transmission and access control. Supports integration with enterprise security systems for centralized management.

Kafka is ideal for building real-time data pipelines that process and analyze streams of data, such as monitoring systems, log aggregation, and fraud detection.

Suitable for implementing event-driven architectures where events are captured and processed in real time, enabling responsive and scalable applications.

Kafka can act as a central hub for data integration, facilitating the movement of data between different systems, applications, and databases.

Acts as a robust messaging backbone for microservices, ensuring reliable communication and decoupling of services.

Kafka Streams allows for the development of applications that process, transform, and analyze data streams in real time, supporting advanced analytics and decision-making.

Kafka docs