It's important to decide the time interval for Spark Streaming, based on your use case and data processing requirements. We usually store these results into a data store for further analytics and to generate reports and dashboards or sending event based alerts. The results of these RDD operations are returned in batches. Then we can process these RDDs using the operations like map, reduce, reduceByKey, join and window. The way Spark Streaming works is it divides the live stream of data into batches (called microbatches) of a pre-defined interval (N seconds) and then treats each batch of data as Resilient Distributed Datasets (RDDs). Spark Ecosystem with Spark Streaming Library Spark Streaming makes it easy to build fault-tolerant processing of real-time data streams.įigure 1 below shows how Spark Streaming fits into overall Apache Spark ecosystem.įigure 1. Spark Streaming is an extension of core Spark API. We’ll focus on Spark Streaming in this article. There are different streaming data processing frameworks as listed below: If we are building applications to collect, process and analyze streaming data in real time, we need to take different design considerations into account than when we are working on applications used to process the static batch data. Streaming data processing applications help with live dashboards, real-time online recommendations, and instant fraud detection. Some of the examples of streaming data are user activity on websites, monitoring data, server logs, and other event data. Streaming data is basically a continuous group of data records generated from sources like sensors, server traffic and online searches.
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