We know how you like to read about big data technology. That’s why we need to know about Stream Processing purpose and use cases. It is used to query continuous data stream and detect conditions, quickly, within a small time period from the time of receiving the data. Enjoy!
It is also called by many names: real-time analytics, streaming analytics, Complex Event Processing, real-time streaming analytics, and event processing. Although some terms historically had differences, now tools (frameworks) have converged under term stream processing.
Following are some of the secondary reasons for using Stream Processing.
Reasons 1: Some data naturally comes as a never-ending stream of events. To do batch processing, you need to store it, stop data collection at some time and processes the data.
Reason 2: Batch processing lets the data build up and try to process them at once while stream processing process data as they come in hence spread the processing over time.
Reason 3: Sometimes data is huge and it is not even possible to store it. Stream processing let you handle large fire horse style data and retain only useful bits.
Reason 4: Finally, there are a lot of streaming data available ( e.g. customer transactions, activities, website visits) and they will grow faster with IoT use cases ( all kind of sensors).
If you want to build the App yourself, place events in a message broker topic (e.g. ActiveMQ, RabbitMQ, or Kafka), write code to receive events from topics in the broker ( they become your stream) and then publish results back to the broker. Such a code is called an actor.
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And don’t miss the AI & Big Data Day 2019! You should be there as well!