March 2019
Beginner to intermediate
182 pages
4h 6m
English
Content preview from Hands-On Big Data Analytics with PySpark
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Summary
In this chapter, we have learned how to calculate averages with map and reduce. We also learned faster average computations with aggregate. Finally, we learned that pivot tables allow us to aggregate data based on different values of features, and that, with pivot tables in PySpark, we can leverage handy functions, such as reducedByKey or countByKey.
In the next chapter, we will learn about MLlib, which involves machine learning, which is a very hot topic.
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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.