Highly Parallel Programming with Apache Spark

Tutorials – Apache Spark

Article from Issue 202/2017

Churn through lots of data with cluster computing on Apache's Spark platform.

As a society, we're creating more data than ever before. We're monitoring everything from the planet's weather to the performance of our computers, and we're storing all this information. But how do you process all this data? On a single machine, you can get a few terabytes of disk space and a few hundred gigabytes of memory (at least, you can if your pockets are deep enough), but how do you churn through a petabyte of raw ones and zeros? Basically, you're going to need more than one computer, and you're going to look for a method of running your programs on many machines at the same time: Apache Spark [1].

Before you run off and buy a rack of servers, slow down! We're going to start by introducing Spark on a single machine. Once you've mastered the basics, you can scale up.

Spark is a data processing engine that is often used with Hadoop for managing large amounts of data in a highly distributed manner. If you move forward with Spark, you're probably going to end up with a complete Hadoop setup; however, that's also getting ahead of ourselves. We can start Spark as a standalone service on a single computer.


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