Covering the Basics: Spark as a Service

Developed by the Apache Software Foundation, which specializes in open source software and has taken a particular fancy to big data analytical tools, Spark is an in-memory distributed processing and analytical platform. Spark originated as a class project at the University of California at Berkeley to fill in some gaps that existed in the big data technologies of the time. That was 2009. Since then, it has matured into a fully-functional platform that is utilized by many organizations across various industries. It is used to build big data analytics applications using the most popular languages, such as Java, Python, Scala, and R.

Spark Versus MapReduce

Spark is most widely used inside the growing Hadoop ecosystem, and is seen as the biggest competitor to MapReduce. MapReduce is the go-to parallel big data processing system, but Spark is much faster. Both of these platforms run on clusters, but Spark can run on a few hundreds of nodes per cluster, whereas MapReduce is able to run over tens of thousands of nodes. Both Spark and MapReduce run on YARN, and both take advantage of data that is stored in the HDFS. However, MapReduce is mostly used for mass batch processing, while Spark primarily uses in-memory storage and processing. Most industry experts expect that Apache Spark will eventually replace MapReduce entirely, which is backed up by the fact that there has already been significant progress made to push the number of nodes that Spark can leverage simultaneously. But for now, MapReduce remains the go-to platform for organizations that are serious about big data and analytics.

Spark as a Service

As most products these days, if you can get it, you can probably get it as a Service. Like most as a Service products, that means that you can take advantage of it without the hardware investments and full-scale adoption and implementation. There are already providers offering Spark as a Service, ideal for short-term data analytics projects that can be set up quickly with a low TOC and high ROI.

Since building and configuring Spark clusters is the most costly and time-consuming (as well as resource intensive) parts of leveraging this platform, Spark as a Service speeds up the process and eliminates most of the cost and effort required. Usually, you just inform your service provider of how much memory you need, and they will size and configure the cluster for you. These vendors also offer supplemental services, including security for the environment, monitoring of the processes, and resource monitoring. Most vendors will also give you a choice when it comes to which language you use, such as SQL, Python, Scala, etc. Some even allow you to generate data visualizations and dashboards for the analytics right inside their service platform.

While Spark as a Service is an obvious choice for temporary or smaller analytics projects, it’s also an excellent foot in the door for organizations that want to see what big data and analytics can do for them before making massive investments.

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