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Learning Spark SQL
Learning Spark SQL

Learning Spark SQL: Architect streaming analytics and machine learning solutions

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Learning Spark SQL

Getting Started with Spark SQL

Spark SQL is at the heart of all applications developed using Spark. In this book, we will explore Spark SQL in great detail, including its usage in various types of applications as well as its internal workings. Developers and architects will appreciate the technical concepts and hands-on sessions presented in each chapter, as they progress through the book.

In this chapter, we will introduce you to the key concepts related to Spark SQL. We will start with SparkSession, the new entry point for Spark SQL in Spark 2.0. Then, we will explore Spark SQL's interfaces RDDs, DataFrames, and Dataset APIs. Later on, we will explain the developer-level details regarding the Catalyst optimizer and Project Tungsten.

Finally, we will introduce an exciting new feature in Spark 2.0 for streaming applications, called Structured Streaming. Specific hands-on exercises (using publicly available Datasets) are presented throughout the chapter, so you can actively follow along as you read through the various sections.

More specifically, the sections in this chapter will cover the following topics along with practice hands-on sessions:

  • What is Spark SQL?
  • Introducing SparkSession
  • Understanding Spark SQL concepts
    • Understanding RDDs, DataFrames, and Datasets
    • Understanding the Catalyst optimizer
    • Understanding Project Tungsten
  • Using Spark SQL in continuous applications
  • Understanding Structured Streaming internals
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Key benefits

  • 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.

Description

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.

Who is this book 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
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Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 07, 2017
Length: 452 pages
Edition : 1st
Language : English
ISBN-13 : 9781785888359
Vendor :
Apache
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Product Details

Publication date : Sep 07, 2017
Length: 452 pages
Edition : 1st
Language : English
ISBN-13 : 9781785888359
Vendor :
Apache
Category :
Languages :
Concepts :

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Table of Contents

12 Chapters
Getting Started with Spark SQL Chevron down icon Chevron up icon
Using Spark SQL for Processing Structured and Semistructured Data Chevron down icon Chevron up icon
Using Spark SQL for Data Exploration Chevron down icon Chevron up icon
Using Spark SQL for Data Munging Chevron down icon Chevron up icon
Using Spark SQL in Streaming Applications Chevron down icon Chevron up icon
Using Spark SQL in Machine Learning Applications Chevron down icon Chevron up icon
Using Spark SQL in Graph Applications Chevron down icon Chevron up icon
Using Spark SQL with SparkR Chevron down icon Chevron up icon
Developing Applications with Spark SQL Chevron down icon Chevron up icon
Using Spark SQL in Deep Learning Applications Chevron down icon Chevron up icon
Tuning Spark SQL Components for Performance Chevron down icon Chevron up icon
Spark SQL in Large-Scale Application Architectures Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(4 Ratings)
5 star 50%
4 star 0%
3 star 25%
2 star 0%
1 star 25%
Minta Thomas Jan 28, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
'Learning Spark SQL' authored by Aurobindo Sarkar, provides a practical resource for learning Spark SQL. The book talks about the design, implementation and deliver of streaming applications; machine learning pipelines using Spark SQL API. The book includes the applications of Spark SQL in data exploration, data munging, data streaming and machine learning. In addition, it provides R code for using Spark SQL with Spark R, basically for text analytics and preprocessing. Author demonstrates the basic concepts of deep learning models, overview of these libraries in Spark, with simple and relevant codes. The final chapter would help us to identify uses cases where the Spark SQL can be used in large scale Spark based architecture, it is a complete guide for developing web based large scale stream applications. I would like to thank and appreciate the Author for his great effort to make this informative resources available for the data science community.
Amazon Verified review Amazon
n1tk Feb 25, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very good book but will be great to be pyspark and python based instead of scala and R
Amazon Verified review Amazon
Marcelo Marques Nov 04, 2017
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Farei enough. Well writen and with lots of Good exames for clarification and pratical contextualization. Limited to Scala language, But it doesnt mean a problem
Amazon Verified review Amazon
akash jain Oct 15, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Worst Book. No detail content about any topics. Its better if i will study from google.Complete waste of money. I purchased the online copy, so i dont know how to return.
Amazon Verified review Amazon
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