Learning Spark SQL

Sarkar, Aurobindo

ISBN 10: 1785888358 / ISBN 13: 9781785888359

Verlag: Packt Publishing, 2017

Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less. Bestandsnummer des Verkäufers G1785888358I2N00

Inhaltsangabe:

Design, implement, and deliver successful streaming applications, machine learning pipelines and graph applications using Spark SQL API

About This Book

• Learn about the design and implementation of streaming applications, machine learning pipelines, deep learning, and large-scale graph processing applications using Spark SQL APIs and Scala.
• Learn data exploration, data munging, and how to process structured and semi-structured data using real-world datasets and gain hands-on exposure to the issues and challenges of working with noisy and "dirty" real-world data.
• Understand design considerations for scalability and performance in web-scale Spark application architectures.

Who This Book Is For

If you are a developer, engineer, or an architect and want to learn how to use Apache Spark in a web-scale project, then this is the book for you. It is assumed that you have prior knowledge of SQL querying. A basic programming knowledge with Scala, Java, R, or Python is all you need to get started with this book.

What You Will Learn

• Familiarize yourself with Spark SQL programming, including working with DataFrame/Dataset API and SQL
• Perform a series of hands-on exercises with different types of data sources, including CSV, JSON, Avro, MySQL, and MongoDB
• Perform data quality checks, data visualization, and basic statistical analysis tasks
• Perform data munging tasks on publically available datasets
• Learn how to use Spark SQL and Apache Kafka to build streaming applications
• Learn key performance-tuning tips and tricks in Spark SQL applications
• Learn key architectural components and patterns in large-scale Spark SQL applications

In Detail

In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems.

This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL.

It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help you understand the methods used to implement typical use-cases for various types of applications. You will get a walkthrough of the key concepts and terms that are common to streaming, machine learning, and graph applications. You will also learn key performance-tuning details including Cost Based Optimization (Spark 2.2) in Spark SQL applications. Finally, you will move on to learning how such systems are architected and deployed for a successful delivery of your project.

Style and approach

This book is a hands-on guide to designing, building, and deploying Spark SQL-centric production applications at scale.

Über die Autorin bzw. den Autor: Aurobindo Sarkar is currently the country head (India Engineering Center) for ZineOne Inc. With a career spanning over 25 years, he has consulted at some of the leading organizations in India, the US, the UK, and Canada. He specializes in real-time architectures, machine learning, cloud engineering, and big data analytics. Aurobindo has been actively working as a CTO in technology startups for over 8 years now. He also teaches machine learning courses at business schools and corporates.

Bibliografische Details

Titel: Learning Spark SQL
Verlag: Packt Publishing
Erscheinungsdatum: 2017
Einband: Paperback
Zustand: As New
Zustand des Schutzumschlags: No Jacket