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Big Data Analysis with Python

You're reading from   Big Data Analysis with Python Combine Spark and Python to unlock the powers of parallel computing and machine learning

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789955286
Length 276 pages
Edition 1st Edition
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Authors (3):
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Ivan Marin Ivan Marin
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Ivan Marin
Sarang VK Sarang VK
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Sarang VK
Ankit Shukla Ankit Shukla
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Ankit Shukla
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Table of Contents (11) Chapters Close

Big Data Analysis with Python
Preface
1. The Python Data Science Stack FREE CHAPTER 2. Statistical Visualizations 3. Working with Big Data Frameworks 4. Diving Deeper with Spark 5. Handling Missing Values and Correlation Analysis 6. Exploratory Data Analysis 7. Reproducibility in Big Data Analysis 8. Creating a Full Analysis Report Appendix

Writing Parquet Files


The Parquet data format (https://parquet.apache.org/) is binary, columnar storage that can be used by different tools, including Hadoop and Spark. It was built to support compression, to enable higher performance and storage use. Its column-oriented design helps with data selection for performance, as only the data in the required columns are retrieved, instead of searching for the data and discarding values in rows that are not required, reducing the retrieval time for big data scenarios, where the data is distributed and on disk. Parquet files can also be read and written by external applications, with a C++ library, and even directly from pandas.

The Parquet library is currently being developed with the Arrow project (https://arrow.apache.org/).

When considering more complex queries in Spark, storing the data in Parquet format can increase performance, especially when the queries need to search a massive dataset. Compression helps to decrease the data volume that needs...

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