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 showed some examples of how to write your Spark code in Python and R. These are the most popular programming languages in the data scientist community.
We covered the motivation of using PySpark and SparkR for big data analytics with almost similar ease with Java and Scala. We discussed how to install these APIs on their popular IDEs such as PyCharm for PySpark and RStudio for SparkR. We also showed how to work with DataFrames and RDDs from these IDEs. Furthermore, we discussed how to execute Spark SQL queries from PySpark and SparkR. Then we also discussed how to perform some analytics with visualization of the dataset. Finally, we saw how to use UDFs with PySpark with examples.
Thus, we have discussed several ...
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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.