Essential PySpark for Scalable Data Analytics A beginner's guide to harnessing the power and ease of PySpark 3

  • en
  • Broché
  • 9781800568877
  • 29 octobre 2021
  • 322 pages
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Résumé

Essential PySpark for Scalable Data Analytics is an introduction for anyone new to the distributed computing model. You'll learn to unlock the analytics world by building end-to-end data processing pipelines, starting with data ingestion, cleansing, and integration, through to data visualization and building and operationalizing predictive models.



Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale

Key Features
  • Discover how to convert huge amounts of raw data into meaningful and actionable insights
  • Use Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analytics
  • Perform data ingestion, cleansing, and integration for ML, data analytics, and data visualization
Book Description

Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework.

Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas.

By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems.

What you will learn
  • Understand the role of distributed computing in the world of big data
  • Gain an appreciation for Apache Spark as the de facto go-to for big data processing
  • Scale out your data analytics process using Apache Spark
  • Build data pipelines using data lakes, and perform data visualization with PySpark and Spark SQL
  • Leverage the cloud to build truly scalable and real-time data analytics applications
  • Explore the applications of data science and scalable machine learning with PySpark
  • Integrate your clean and curated data with BI and SQL analysis tools
Who this book is for

This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.

Spécifications produit

Contenu

Langue
en
Version
Broché
Date de sortie initiale
29 octobre 2021
Nombre de pages
322

Personnes impliquées

Auteur principal
Sreeram Nudurupati
Editeur principal
Packt Publishing Limited

Informations sur le fabricant

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Hauteur de l'emballage
20 mm
Largeur d'emballage
186 mm
Largeur du produit
75 mm
Livre d‘étude
Oui
Longueur d'emballage
227 mm
Longueur du produit
93 mm
Poids de l'emballage
703 g
Porno
Non
Édition
ed

EAN

EAN
9781800568877

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