Streaming Systems

Streaming Data Platforms: Kafka, Pulsar, and Beyond Streaming data platforms help teams publish and consume a steady flow of events. The two most popular open-source options are Apache Kafka and Apache Pulsar. Both store streams and support real-time processing, but they approach the problem with different design goals. Kafka focuses on a durable log with broad ecosystem support, while Pulsar separates storage and compute, offering strong multi-tenant capabilities and built-in geo-replication. ...

Building Real-Time Pipelines with Apache Kafka Streaming data lets teams react quickly to events, from sensor alerts to user actions. Apache Kafka provides a reliable backbone for these flows. It stores streams of records in topics, serves many producers and consumers, and scales as data grows. With Kafka, you can decouple data producers from readers while keeping order and durability. Kafka works with a few core ideas. A topic is a named stream of records. Each topic may be divided into partitions, which enables parallel reads and writes. Producers publish records to topics, and each record is stored with an offset, a stable position within a partition. Consumers read from topics, often in groups, to share the work of processing data. Messages are stored for a configured time or size, so new readers can catch up even after a delay. This design supports both real-time analytics and batched workflows without losing data. ...