In the streaming solutions space, it all begins with the event driven architecture. This basically includes events (what triggers everything), the handlers (responsible for taking action) & the event loop (for coordinating). When things get more involved & complicated with multiple event streams/ sources, etc. solutions move into the cep space.
Another very popular programming methodology in recent times is Reactive programming. This in some senses is a special case of event driven programming with the focus on data change (as the event) & the reactive step to do other downstream data changes (as the handlers).
A whole bunch of frameworks for streaming solutions have emerged from the Big Data ecosystem such as Storm, Spark Streaming, Flink, etc. These allow for quick development of streaming solutions using high level abstractions. Even Solr has a streaming expression support now for building distributed streaming search solutions.
Outside of these frameworks, Akka Streams seems promising. It's built on top of Akka's robus Actor model & the Reactive Streams api. Solutions such as Gear Pump can provide a sense of the ground up solutions possible with Akka Streams.
Another very popular programming methodology in recent times is Reactive programming. This in some senses is a special case of event driven programming with the focus on data change (as the event) & the reactive step to do other downstream data changes (as the handlers).
A whole bunch of frameworks for streaming solutions have emerged from the Big Data ecosystem such as Storm, Spark Streaming, Flink, etc. These allow for quick development of streaming solutions using high level abstractions. Even Solr has a streaming expression support now for building distributed streaming search solutions.
Outside of these frameworks, Akka Streams seems promising. It's built on top of Akka's robus Actor model & the Reactive Streams api. Solutions such as Gear Pump can provide a sense of the ground up solutions possible with Akka Streams.
