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Spark for Data Science

You're reading from   Spark for Data Science Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785885655
Length 344 pages
Edition 1st Edition
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Authors (2):
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Bikramaditya Singhal Bikramaditya Singhal
Author Profile Icon Bikramaditya Singhal
Bikramaditya Singhal
Srinivas Duvvuri Srinivas Duvvuri
Author Profile Icon Srinivas Duvvuri
Srinivas Duvvuri
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Toc

Table of Contents (12) Chapters Close

Preface 1. Big Data and Data Science – An Introduction FREE CHAPTER 2. The Spark Programming Model 3. Introduction to DataFrames 4. Unified Data Access 5. Data Analysis on Spark 6. Machine Learning 7. Extending Spark with SparkR 8. Analyzing Unstructured Data 9. Visualizing Big Data 10. Putting It All Together 11. Building Data Science Applications

Datasets

Apache Spark Datasets are an extension of the DataFrame API that provide a type-safe object-oriented programming interface. This API was first introduced in the 1.6 release. Spark 2.0 version brought out unification of DataFrame and Dataset APIs. DataFrame becomes a generic, untyped Dataset; or a Dataset is a DataFrame with an added structure. The term "structure" in this context refers to a pattern or an organization of underlying data, more like a table schema in RDBMS parlance. The structure imposes a limit on what can be expressed or contained in the underlying data. This in turn enables better optimizations in memory organization as well as physical execution. Compile-time type checking leads to catching errors earlier than during runtime. For example, a type mismatch in a SQL comparison does not get caught until runtime, whereas it would be caught during compile time itself if it were expressed as a sequence of operations on Datasets. However, the inherent dynamic...

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