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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 data scientist and Spark features


One of the interesting questions relevant to this book is, "What do data scientists want?" It is a question that is being discussed and debated in many blogs. A short answer is as follows:

  • The ability to explore, model, and reason data at scale-because many of their algorithms get asymptotically better with data, and so, a small Dataset sample is not enough for exploring different algorithms

  • The ability to deploy without a lot of impedance

  • The facility to evolve models once they are in production and the real world is using them

In short, all we ask for is the shortest path from the lab to the factory, enabling a data scientist DevOps person! The following screenshot (combining talks from Josh Willis and Ian Buss), which displays The Sense & Sensibility of a Data Scientist DevOps, succinctly shows the value of Apache Spark to a data scientist by addressing three points:

Who is this data scientist DevOps person?

Of course, we really do not want to start...

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