Stream Processing

Stream processing is a data processing technique that involves continuous processing of data in motion, also known as data streams. It involves real-time processing of data as it is generated, with the ability to handle high volumes of data and provide immediate insights. Stream processing is used in various applications such as fraud detection, real-time analytics, and IoT devices. It involves the use of distributed systems and parallel processing to handle the high volume of data. Stream processing can be done in two ways: real-time processing and micro-batch processing. Real-time processing involves processing data as soon as it is generated, while micro-batch processing involves processing data in small batches at regular intervals. Stream processing is an important technique in the field of data science and artificial intelligence as it enables real-time decision making and provides immediate insights into data.
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