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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

Continuous applications


We discussed how unified data access is empowered by Spark. It lets you process data in a myriad of ways to build end-to-end continuous applications by enabling various analytic workloads, such as ETL processing, ad hoc queries, online machine learning modeling, or to generate necessary reports... all of this in a unified way by letting you work on both static as well as streaming data using a high-level, SQL-like API. In this way, Structured Streaming has substantially simplified the development and maintenance of real-time, continuous applications.

Courtesy: Databricks

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