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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

Apache Spark - the full stack


With all of this background information behind us, let's take a quick look at the full Spark stack (shown in the following diagram), which used to be a lot simpler, showing how the Spark ecosystem is continually evolving:

The Spark stack currently includes the following features:

  • It provides the Spark SQL feature. This feature uses SQL for data manipulation while maintaining the underlying Spark computations. It also provides the vital interface via exposing the Datasets to external systems through JDBC/ODBC, arguably the best value of Spark SQL.

  • Advanced analytics, which is still evolving; look out for features such as parameter server and neural networks in the later versions of Spark.

  • It provides the Dataset/DataFrame API, of course. It is one of parts we are focusing on in this book and we will see more of it in the following chapters.

  • The catalyst optimizer is an interesting beast. It is the proverbial software layer that separates a declarative API/interface...

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