How to run PySpark on a Cluster II PySpark II PySpark Tutorial II KSR Datavizon

How to run PySpark on a Cluster II PySpark

Spark is a fast and general-purpose cluster computing platform that works well for many types of parallelizable tasks data scientists need to perform every day. Spark extends MapReduce, which only has two operators, to include a much wider set of interactive analytical query and stream processing options. Probably the biggest advantage of Spark is its ability to run computations in memory. The Spark website says it runs programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.

Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools. These two qualities are key to the worlds of big data and machine learning, which require the marshalling of massive computing power to crunch through large data stores. Spark also takes some of the programming burdens of these tasks off the shoulders of developers with an easy-to-use API that abstracts away much of the grunt work of distributed computing and big data processing.

#pyspark #Pysparktutorial #ksrdatavizon

0:00 Introduction
0:20 How PySpark runs on a Cluster
0:30 PySpark Cluster
0:50 Work Node
2:35 Scheduler
2:55 Application Manager

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