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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
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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

Using the Avro data model in Parquet

Parquet is a kind of highly efficient columnar storage, but it is also relatively new. Avro (https://avro.apache.org) is a widely used row-based storage format. This recipe showcases how we can retain the older and flexible Avro schema in our code but still use the Parquet format during storage.

The Spark MR project (yes, the one that has the Parquet tools we saw in the previous recipe) has converters for almost all the popular data formats. These model converters take your format and convert it into Parquet format before causing it to persist.

How to do it…

In this recipe, we'll use the Avro data model and serialize the data in a Parquet file. The recipe involves the following steps:

  1. Create the Avro Model.
  2. Generate Avro objects using the sbt avro plugin.
  3. Construct the RDD of your generated object (StudentAvro) from Students.csv.
  4. Save the RDD[StudentAvro] in a Parquet file.
  5. Read the file back for verification.
  6. Use Parquet-tools to verify.

Creation...

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