RidgeRun Metadata/Metadata Integration with Messaging Systems/Kafka

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Apache Kafka

Introduction

Apache Kafka is a distributed event streaming platform used to build real-time data pipelines and streaming applications. Originally developed at LinkedIn and later open-sourced through the Apache Software Foundation, Kafka is designed to handle high-throughput, fault-tolerant, and low-latency data communication between systems.

Kafka works as a publish/subscribe messaging system, where producers send messages to topics, and consumers subscribe to those topics to receive data in real time. Unlike traditional messaging queues, Kafka is persistent and distributed, which allows it to serve as a reliable backbone for critical data infrastructure.

Key Features

High throughput and low latency: Designed to handle millions of messages per second with very low latency, even at scale.
Durability and persistence: Messages are written to disk and can be retained for configurable periods, allowing for replay and auditing.
Scalability: Kafka supports horizontal scaling by partitioning data across multiple brokers and consumers.
Fault tolerance: Replication across brokers ensures continued operation even in the case of hardware or node failure.
Stream processing integration: Built-in APIs (Kafka Streams, ksqlDB) support real-time transformation and analysis of data as it flows.

Apache Kafka is particularly useful in systems that demand reliable, scalable, and high-performance data communication. It is ideal for environments with continuous data generation and the need to process, analyze, or store that data in real time. Kafka supports decoupled architectures, allowing producers and consumers to operate independently while sharing a common data stream.

Its durability and ability to replay historical data also make it a powerful tool for auditing, debugging, and machine learning pipelines. Kafka is not only used for logging and telemetry, but also as a central hub for streaming architectures across cloud-native, big data, and enterprise systems.

Common Use Cases

Log and telemetry aggregation from multiple services or devices.
Streaming analytics pipelines that perform filtering, enrichment, or aggregation of real-time data.
Integration of microservices through event-driven communication.
Machine learning pipelines, where feature extraction or inference relies on continuous data streams.
Video metadata processing, such as bounding boxes, object labels, or sensor data extracted from SEI streams, published into Kafka topics for consumption by AI models, alerting systems, or cloud dashboards.