Hands-On Big Data Analytics with PySpark (Paperback)

Rudy Lai

ISBN 10: 183864413X / ISBN 13: 9781838644130

Editore: Packt Publishing Limited, Birmingham, 2019

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Paperback. Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobsKey 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 DescriptionApache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs.You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark.By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.What you will learnGet practical big data experience while working on messy datasetsAnalyze patterns with Spark SQL to improve your business intelligenceUse PySpark's interactive shell to speed up development timeCreate highly concurrent Spark programs by leveraging immutabilityDiscover ways to avoid the most expensive operation in the Spark API: the shuffle operationRe-design your jobs to use reduceByKey instead of groupByCreate robust processing pipelines by testing Apache Spark jobsWho this book is forThis book is for developers, data scientists, business analysts, or anyone who needs to reliably analyze large amounts of large-scale, real-world data. Whether you're tasked with creating your company's business intelligence function or creating great data platforms for your machine learning models, or are looking to use code to magnify the impact of your business, this book is for you. In this book, you'll learn to implement some practical and proven techniques to improve aspects of programming and administration in Apache Spark. Techniques are demonstrated using practical examples and best practices. You will also learn how to use Spark and its Python API to create performant analytics with large-scale data. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9781838644130

Dati bibliografici

• Titolo: Hands-On Big Data Analytics with PySpark (Paperback)
• Editore: Packt Publishing Limited, Birmingham
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 183864413X
• ISBN 13: 9781838644130
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs

Key Features:

- Work with large amounts of agile data using distributed datasets and in-memory caching

- Source data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3

- Employ the easy-to-use PySpark API to deploy big data Analytics for production

Book Description:

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs.

You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark.

By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.

What You Will Learn:

- Get practical big data experience while working on messy datasets

- Analyze patterns with Spark SQL to improve your business intelligence

- Use PySpark s interactive shell to speed up development time

- Create highly concurrent Spark programs by leveraging immutability

- Discover ways to avoid the most expensive operation in the Spark API: the shuffle operation

- Re-design your jobs to use reduceByKey instead of groupBy

- Create robust processing pipelines by testing Apache Spark jobs

Who this book is for:

This book is for developers, data scientists, business analysts, or anyone who needs to reliably analyze large amounts of large-scale, real-world data. Whether you're tasked with creating your company's business intelligence function or creating great data platforms for your machine learning models, or are looking to use code to magnify the impact of your business, this book is for you.