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  • 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 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.

  • 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.
Page Count 452
Course Length 13 hours 33 minutes
ISBN 9781785888359
Date Of Publication 6 Sep 2017


Aurobindo Sarkar

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.