July 2017
Intermediate to advanced
796 pages
18h 55m
English
Content preview from Scala and Spark for Big Data Analytics
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Summary
In this chapter, we have introduced graph theory using Facebook as an example; Apache Spark's graph processing library GraphX, VertexRDD, and EdgeRDDs; graph operators, aggregateMessages, TriangleCounting, and the Pregel API; and use cases such as the PageRank algorithm. We have also seen the traveling salesman problem and connected components and so on. We have seen how the GraphX API can be used to develop graph processing algorithms at scale.
In Chapter 11, Learning Machine Learning - Spark MLlib and ML, we will explore the exciting world of Apache Spark's Machine Learning library.
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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.