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Apache Spark is a cluster computing platform designed to be fast and general-purpose. On the speed side, Spark extends the popular MapReduce model to efficiently support more types of computations, including interactive queries and stream processing.

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Spark introduction ppt

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Spark introduction ppt

Indeed, Spark is a technology well worth taking note of and learning about. This article provides an introduction to Spark including use cases and examples. Spark is a general-purpose data processing engine, an API-powered toolkit which data scientists and application developers incorporate into their applica- tions to rapidly query, analyze and transform data at scale. An introduction about the Apache Spark Framework - Here we show you about apache spark. Apache Spark is a unified analytics engine for big data processing also you can, use it interactively from the Scala, Python, R, and SQL shells.
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Here, we will discuss why should we learn Apache spark, why it is emerging as a next-generation generalized engine for large-scale data processing. To get it better, we will also know how spark took over the command in the industry on comparing with mapreduce. 2. Spark uses Hadoop in two ways – one is storage and second is processing. Since Spark has its own cluster management computation, it uses Hadoop for storage purpose only.

As against a common belief, Spark is not a modified version of Hadoopand is not, really, dependent on Hadoop because it has its own cluster management.
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Spark System Requirements (continued) Memory. Spark runs well with anywhere from 8 GB to hundreds of gigabytes of memory per machine. In all cases, we recommend allocating only at most 75% of the memory for Spark; leave the rest for the operating system and buffer cache. Network. When the data is in memory, a lot of Spark applications are

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