Streaming Analytics Platforms

Streaming analytics processes data continuously as it is produced, instead of waiting to collect it into batches. It lets organizations detect events, update dashboards, trigger alerts and feed machine learning models within seconds or milliseconds of the data arriving.

A streaming platform or event broker such as Apache Kafka, Redpanda or Apache Pulsar durably stores and distributes streams of events between systems. A stream processing engine such as Apache Flink, Spark Structured Streaming or Apache Beam computes over those streams, for example filtering, joining and aggregating them. Most real-time architectures use both.

Not always. Kafka and Kafka-compatible platforms are the most common backbone, but cloud services such as Amazon Kinesis, Azure Event Hubs and Google Pub/Sub provide similar capabilities as managed services, and Apache Pulsar is a widely used alternative. The right choice depends on existing skills, ecosystem and operating model.

A streaming database such as Materialize, RisingWave or Timeplus lets you define SQL queries that run continuously and keep results, called materialized views, incrementally up to date as new events arrive. It makes real-time analytics accessible to teams that already work in SQL.

Batch analytics processes accumulated data at intervals and favors completeness and throughput, while streaming analytics processes each event as it arrives and favors low latency. Frameworks such as Apache Beam and Apache Flink support both models, and many organizations combine them.