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Data stream processing is defined as a system performing transformations for creating analytics on data inside a stream. In Part 1 of this series, we defined data streaming to provide an understanding of its importance. In Part 2, we got a bit more technical in explaining data integration and ingestion into one of the more popular stream platforms, Apache Kafka.
Data transaction streaming is managed through many platforms, with one of the most common being Apache Kafka. In our first article in this data streaming series, we delved into the definition of data transaction and streaming and why it is critical to manage information in real-time for the most accurate analytics. As more individuals increase their data literacy and use data to make business decisions, real-time data is becoming a critical factor.
(spainter_vfx/Shutterstock) The terms “real-time data” and “streaming data” are the latest catch phrases being bandied about by almost every data vendor and company. Everyone wants the world to know that they have access to and are using the latest, greatest data for making business decisions. But using the term is not enough. Do your business executives and users know what data streaming is, where the information comes from and why it is important?
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