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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

Product type Paperback

Published in Apr 2019

Publisher Packt

ISBN-13 9781789955286

Length 276 pages

Edition 1st Edition

Languages

Python

Tools

Combine

Concepts

Big Data

Authors (3):

Ivan Marin

Sarang VK

Ankit Shukla

View More author details

Table of Contents (11) Chapters

Big Data Analysis with Python

Preface

1. The Python Data Science Stack

2. Statistical Visualizations FREE CHAPTER

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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Authors (3)

Marin

Marin

Ivan Marin is a systems architect and data scientist working at Daitan Group, a Campinas-based software company. He designs big data systems for large volumes of data and implements machine learning pipelines end to end using Python and Spark. He is also an active organizer of data science, machine learning, and Python in So Paulo, and has given Python for data science courses at university level.

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VK

VK

Sarang VK is a lead data scientist at StraitsBridge Advisors, where his responsibilities include requirement gathering, solutioning, development, and productization of scalable machine learning, artificial intelligence, and analytical solutions using open source technologies. Alongside this, he supports pre-sales and competency.

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Shukla

Shukla

Vishal Shukla is the CEO of Brevitaz Systems (http://brevitaz.com) and a technology evangelist at heart. He is a passionate software scientist and a big data expert. Vishal has extensive experience in designing modular enterprise systems. Since his college days (more than 11 years), Vishal has enjoyed coding in JVM-based languages. He also embraces design thinking and sustainable software development. He has vast experience in architecting enterprise systems in various domains. Vishal is deeply interested in technologies related to big data engineering, analytics, and machine learning. He set up Brevitaz Systems. This company delivers massively scalable and sustainable big data and analytics-based enterprise applications to their global clientele. With varied expertise in big data technologies and architectural acumen, the Brevitaz team successfully developed and re-engineered a number of legacy systems to state-of-the-art scalable systems. Brevitaz has imbibed in its culture agile practices, such as scrum, test-driven development, continuous integration, and continuous delivery, to deliver high-quality products to its clients. Vishal is a music and art lover. He loves to sing, play musical instruments, draw portraits, and play sports, such as cricket, table tennis, and pool, in his free time. You can contact Vishal at vishal.shukla@brevitaz.com and on LinkedIn at https://in.linkedin.com/in/vishalshu. You can also follow Vishal on Twitter at @vishal1shukla2.

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