Essential PySpark for Scalable Data Analytics

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  • Publisher : Packt Publishing Ltd
  • Release : 29 October 2021
  • ISBN : 9781800563094
  • Page : 322 pages
  • Rating : 4.5/5 from 103 voters

Essential PySpark for Scalable Data Analytics Book PDF summary

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.

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Essential PySpark for Scalable Data Analytics

Essential PySpark for Scalable Data Analytics
  • Author : Sreeram Nudurupati
  • Publisher : Packt Publishing Ltd
  • Release Date : 2021-10-29
  • ISBN : 9781800563094
DOWNLOAD BOOKEssential PySpark for Scalable Data Analytics

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

Learning Spark

Learning Spark
  • Author : Jules S. Damji,Brooke Wenig,Tathagata Das,Denny Lee
  • Publisher : O'Reilly Media
  • Release Date : 2020-07-16
  • ISBN : 9781492050018
DOWNLOAD BOOKLearning Spark

Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets,

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

Data Engineering with Apache Spark, Delta Lake, and Lakehouse
  • Author : Manoj Kukreja,Danil Zburivsky
  • Publisher : Packt Publishing Ltd
  • Release Date : 2021-10-22
  • ISBN : 9781801074322
DOWNLOAD BOOKData Engineering with Apache Spark, Delta Lake, and Lakehouse

Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key Features Become well-versed with the core concepts of Apache Spark and Delta Lake for building data platforms Learn how to ingest, process, and analyze data that can be later used for training machine learning models Understand how to operationalize data models in production using curated data Book Description In

Learning PySpark

Learning PySpark
  • Author : Tomasz Drabas,Denny Lee
  • Publisher : Packt Publishing Ltd
  • Release Date : 2017-02-27
  • ISBN : 9781786466259
DOWNLOAD BOOKLearning PySpark

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0 About This Book Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0 Develop and deploy efficient, scalable real-time Spark solutions Take your understanding of using Spark with Python to the next level with this jump start guide Who This Book Is For If you are a Python developer who wants to learn about

Hands-On Big Data Analytics with PySpark

Hands-On Big Data Analytics with PySpark
  • Author : Rudy Lai,Bartłomiej Potaczek
  • Publisher : Packt Publishing Ltd
  • Release Date : 2019-03-29
  • ISBN : 9781838648831
DOWNLOAD BOOKHands-On Big Data Analytics with PySpark

Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs Key FeaturesWork with large amounts of agile data using distributed datasets and in-memory cachingSource data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3Employ the easy-to-use PySpark API to deploy big data Analytics for productionBook Description Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One

PySpark Cookbook

PySpark Cookbook
  • Author : Denny Lee,Tomasz Drabas
  • Publisher : Packt Publishing Ltd
  • Release Date : 2018-06-29
  • ISBN : 9781788834254
DOWNLOAD BOOKPySpark Cookbook

Combine the power of Apache Spark and Python to build effective big data applications Key Features Perform effective data processing, machine learning, and analytics using PySpark Overcome challenges in developing and deploying Spark solutions using Python Explore recipes for efficiently combining Python and Apache Spark to process data Book Description Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for

Data Analytics with Hadoop

Data Analytics with Hadoop
  • Author : Benjamin Bengfort,Jenny Kim
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2016-06
  • ISBN : 9781491913765
DOWNLOAD BOOKData Analytics with Hadoop

Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and

Learning Spark

Learning Spark
  • Author : Holden Karau,Andy Konwinski,Patrick Wendell,Matei Zaharia
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2015-01-28
  • ISBN : 9781449359058
DOWNLOAD BOOKLearning Spark

Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have

Machine Learning in Python

Machine Learning in Python
  • Author : Michael Bowles
  • Publisher : John Wiley & Sons
  • Release Date : 2015-03-30
  • ISBN : 9781118961742
DOWNLOAD BOOKMachine Learning in Python

This book shows readers how they can successfully analyze data using only two core machine learning algorithms---and how to do so using the popular Python programming language. These algorithms deal with common scenarios faced by all data analysts and data scientists. This book focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers a multitude of use cases (what ad to place on a web page, predicting prices in securities markets,

Scala and Spark for Big Data Analytics

Scala and Spark for Big Data Analytics
  • Author : Md. Rezaul Karim,Sridhar Alla
  • Publisher : Packt Publishing Ltd
  • Release Date : 2017-07-25
  • ISBN : 9781783550500
DOWNLOAD BOOKScala and Spark for Big Data Analytics

Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book Learn Scala's sophisticated type system that combines Functional Programming and object-oriented concepts Work on a wide array of applications, from simple batch jobs to stream processing and machine learning Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to

Applied Data Science Using PySpark

Applied Data Science Using PySpark
  • Author : Ramcharan Kakarla,Sundar Krishnan,Sridhar Alla
  • Publisher : Apress
  • Release Date : 2021-01-01
  • ISBN : 1484264991
DOWNLOAD BOOKApplied Data Science Using PySpark

Discover the capabilities of PySpark and its application in the realm of data science. This comprehensive guide with hand-picked examples of daily use cases will walk you through the end-to-end predictive model-building cycle with the latest techniques and tricks of the trade. Applied Data Science Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics of PySpark focusing on data manipulation. We make you comfortable with the language and then

Data Science Solutions with Python

Data Science Solutions with Python
  • Author : Tshepo Chris Nokeri
  • Publisher : Apress
  • Release Date : 2021-10-26
  • ISBN : 1484277619
DOWNLOAD BOOKData Science Solutions with Python

Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and

Spark: The Definitive Guide

Spark: The Definitive Guide
  • Author : Bill Chambers,Matei Zaharia
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2018-02-08
  • ISBN : 9781491912294
DOWNLOAD BOOKSpark: The Definitive Guide

Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system

Mastering Spark with R

Mastering Spark with R
  • Author : Javier Luraschi,Kevin Kuo,Edgar Ruiz
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2019-10-07
  • ISBN : 9781492046325
DOWNLOAD BOOKMastering Spark with R

If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems. Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R

Machine Learning with PySpark

Machine Learning with PySpark
  • Author : Pramod Singh
  • Publisher : Apress
  • Release Date : 2018-12-14
  • ISBN : 9781484241318
DOWNLOAD BOOKMachine Learning with PySpark

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You’ll also see unsupervised machine