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Scala Data Analysis Cookbook (new)

You're reading from   Scala Data Analysis Cookbook (new) Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781784396749
Length 254 pages
Edition 1st Edition
Languages
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Author (1):
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Arun Manivannan Arun Manivannan
Author Profile Icon Arun Manivannan
Arun Manivannan
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Table of Contents (9) Chapters Close

Preface 1. Getting Started with Breeze 2. Getting Started with Apache Spark DataFrames FREE CHAPTER 3. Loading and Preparing Data – DataFrame 4. Data Visualization 5. Learning from Data 6. Scaling Up 7. Going Further Index

Storing data as Parquet files

Parquet (https://parquet.apache.org/) is rapidly becoming the go-to data storage format in the world of big data because of the distinct advantages it offers:

  • It has a column-based representation of data. This is better represented in a picture, as follows:
    Storing data as Parquet files

    As you can see in the preceding screenshot, Parquet stores data in chunks of rows, say 100 rows. In Parquet terms, these are called RowGroups. Each of these RowGroups has chunks of columns inside them (or column chunks). Column chunks can hold more than a single unit of data for a particular column (as represented in the blue box in the first column). For example. Jai, Suri, and Dhina form a single chunk even though they are composed of three single units of data for Name.

    Another unique feature is that these column chunks (groups of a single column's information) can be read independently. Let's consider the following image:

    Storing data as Parquet files

    We can see that the items of column data are stored next to each other in...

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