January 2016
Beginner to intermediate
304 pages
8h 16m
English
book
Big Data Analytics with Spark: A Practitioner’s Guide to Using Spark for Large-Scale Data Processing, Machine Learning, and Graph Analytics, and High-Velocity Data Stream Processing
by Mohammed Guller
Content preview from Big Data Analytics with Spark: A Practitioner’s Guide to Using Spark for Large-Scale Data Processing, Machine Learning, and Graph Analytics, and High-Velocity Data Stream Processing
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CHAPTER 3
Spark Core
Spark is the most active open source project in the big data world. It has become hotter than Hadoop. It is considered the successor to Hadoop MapReduce, which we discussed in Chapter 1. Spark adoption is growing rapidly. Many organizations are replacing MapReduce with Spark.
Conceptually, Spark looks similar to Hadoop MapReduce; both are designed for processing big data. They both enable cost-effective data processing at scale using commodity hardware. However, Spark offers many advantages over Hadoop MapReduce. These are discussed in detail later in this chapter.
This chapter covers Spark core, which forms the foundation ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.