Stream Processing

In order to quickly analyze, filter, transform, or improve the data in real-time, stream processing is a data management technique that entails consuming a continuous data stream. The data is transferred to an application, a data store, or another stream processing engine after it has been processed. The ability for businesses to mix data feed from diverse sources is one reason why stream processing services and architectures are becoming more and more popular. Transactions, stock feeds, website analytics, connected devices, operational databases, weather reports, and other for-profit services are just a few examples of sources. Stream processing's fundamental concepts have been around for a long time, but thanks to a variety of open-source tools and cloud services, they are becoming simpler to put into practice. Using stream processing architectures makes it easier to consume, analyze, and publish data in a secure and reliable manner. Ingestion of data from a publish-subscribe service is the first step in stream processing. The data is then processed, and the results are published back to the publish-subscribe service or another data storage. These can be operations like data analysis, filtering, manipulating, combining, or cleaning.

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