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5 Things Driving the Phenomenal Success of Apache Spark

If Hadoop is the tool that ushered in the era of big data, Spark is the one that's driving the next phase of its evolution. Spark is the brainchild of a group of Berkeley grad students, and brought an entirely new set of use cases for big data and data analytics. Spark is more than just the right thing at the right time, it's a way to get speed out of analytics where before there was power, but nothing in the way of real-time. So, what is sparking the popularity of Spark?

If Hadoop is the tool that ushered in the era of big data, Spark is the one that’s driving the next phase of its evolution. Spark is the brainchild of a group of Berkeley grad students, and brought an entirely new set of use cases for big data and data analytics. Spark is more than just the right thing at the right time, it’s a way to get speed out of analytics where before there was power, but nothing in the way of real-time. So, what is sparking the popularity of Spark?

1. Spark Works With or Without Hadoop

Spark is perfect for managing streaming data, such as that produced by the Internet of Things.

Though Spark is viewed as Hadoop’s primary competitor, the two actually work brilliantly as companions. Hadoop delivers the distributed framework, while Spark takes on the analytical munching must faster than Hadoop’s native MapReduce. But the two can also survive apart, and Spark actually boasts a different set of use cases from Hadoop. Since Spark lacks the distributed framework, you’ll need another storage solution (such as the Metal Cloud), but the two can be used either in concert or as solo acts.

2. Spark Allows for Data Streaming

Hadoop is the go-to solution for batch processing, but Spark is speedy, allowing for streaming data in real time or extremely-nearly real time. Speed and performance are the two most striking differences between Hadoop and Spark. The speediness of Spark makes it practical for numerous use cases that Hadoop is too slow to fulfill.

3. Spark Empowers Machine Learning

One of those use cases is in the realm of machine learning, also known as artificial intelligence. This requires the ability to process streaming data (input into the machine or robot) in real time, and the ability to analyze it quickly in order to build on an ever-growing knowledge base. Without the zippiness of Spark, machine learning wouldn’t have progressed nearly to the point which it has in the realms of product recommendations, cyber security monitoring, and fraud detection, among others.

4. Spark Delivers Ultra-Fast Business Intelligence

Spark is also the ideal candidate for delivering real-time business intel.

This is closely related to machine learning, but not all BI involves machine learning. Spark can conduct fast processing, making it ideal for handling things like online transactions. But it’s also a powerful tool for the security industries, including malware detection, identity theft prevention, and detecting fraudulent activity involving finance and insurance claims.

5. Spark is Way Easier Than MapReduce

Aside from the speedy spunk of Spark, many prefer using it simply to avoid having to use MapReduce. Coding in MapReduce is notoriously difficult and cumbersome. Comparatively, coding for Spark is not difficult at all (if you’re into coding, that is), and is compatible with several languages, including Java, Python, and C.

If you’re interested in taking on big data, you will have a definite need for a highly scalable and flexible storage infrastructure, and Bigstep has it. Discover the first Full Metal Data Lake as a Service in the world. Get 1TB free for life - limited to 100 applicants. Start here.

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