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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

Spark SQL programming


Let's now get our hands dirty and work through various examples. We will start with a simple Dataset and then progressively perform more sophisticated SQL statements. We will use the NorthWind Dataset.

Datasets/DataFrames

In short, Datasets are semantic domain-specific objects, which means they are very rich in terms of typing and they possess all the functions of RDDs. In short, the best of both worlds! A DataFrame is an untyped view into a Dataset, basically a collection of rows. This is useful for doing abstract generic operations on a Dataset, that is, operations that depend only on the positions of elements in a row and other factors. We will learn more in later sections.

Tip

As languages, such as Python and R, do not have compile-time type checking, Datasets and DataFrames are collapsed and called DataFrames.

Another change in 2.0 is sparksession, which replaces sqlcontext, hivecontext, and others. The sparksession instance has a very rich and flexible read method...

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