Big data analysis with Python : combine spark and python to unlock the powers of parallel computing and machine learning / Ivan Marin, Ankit Shukla, Sarang VK.

By:

Marin, Ivan [author.]

Contributor(s):

Material type: Text

TextLanguage: English Publisher: Birmingham : Packt Publishing, 2019Description: 276 pagesISBN:

9781789955286

Subject(s):

Python (Computer program language)

DDC classification:

005.133 23

LOC classification:

QA76.73.P98 .M375 2019

Summary: Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems. The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools. By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs. Learning Objectives: Use Python to read and transform data into different formats — Generate basic statistics and metrics using data on disk — Work with computing tasks distributed over a cluster — Convert data from various sources into storage or querying formats — Prepare data for statistical analysis, visualization, and machine learning — Present data in the form of effective visuals. Approach: Big Data Analysis with Python takes a hands-on approach to understanding how to use Python and Spark to process data and make something useful out of it. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Audience: Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.

Holdings ( 3 )
Title notes ( 1 )

Holdings
Item type	Current library	Call number	Status	Barcode
Book	TBS Barcelona	QA76.73.P98 MAR (Browse shelf(Opens below))	Available	B02371
Book	TBS Barcelona	QA76.73.P98 MAR (Browse shelf(Opens below))	Available	B02372
Book	TBS Barcelona	QA76.73.P98 (Browse shelf(Opens below))	Available	B02370

Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.

The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools.

By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.

Learning Objectives:
Use Python to read and transform data into different formats —
Generate basic statistics and metrics using data on disk —
Work with computing tasks distributed over a cluster —
Convert data from various sources into storage or querying formats —
Prepare data for statistical analysis, visualization, and machine learning —
Present data in the form of effective visuals.

Approach: Big Data Analysis with Python takes a hands-on approach to understanding how to use Python and Spark to process data and make something useful out of it. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.

Audience: Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.