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Fast Data Processing with Spark 2

You're reading from   Fast Data Processing with Spark 2 Accelerate your data for rapid insight

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Product type Paperback
Published in Oct 2016
Publisher Packt
ISBN-13 9781785889271
Length 274 pages
Edition 3rd Edition
Languages
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Authors (2):
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Krishna Sankar Krishna Sankar
Author Profile Icon Krishna Sankar
Krishna Sankar
Holden Karau Holden Karau
Author Profile Icon Holden Karau
Holden Karau
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Toc

Table of Contents (13) Chapters Close

Preface 1. Installing Spark and Setting Up Your Cluster 2. Using the Spark Shell FREE CHAPTER 3. Building and Running a Spark Application 4. Creating a SparkSession Object 5. Loading and Saving Data in Spark 6. Manipulating Your RDD 7. Spark 2.0 Concepts 8. Spark SQL 9. Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists 10. Spark with Big Data 11. Machine Learning with Spark ML Pipelines 12. GraphX

The API organization


As an introduction, the following figure gives you a bird's eye view of the classes and methods that are relevant. Sometimes, one gets lost in the numerous classes and deep hierarchies.

Tip

This organization is definitely something you should get used to. The MLlib library was more straightforward, but with far fewer capabilities. ML is definitely better, once you get the hang of it. It took me a few days to convert the examples; I had to visit/revisit the documentation multiple times. That is why I created the preceding diagram; this should make it easier for you to get a quick roadmap of the process while learning. Once done, it becomes easier to navigate the API documentation and find what one needs. Mastering the pipeline correctly will take a little while.

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