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Apache Spark Quick Start Guide

You're reading from   Apache Spark Quick Start Guide Quickly learn the art of writing efficient big data applications with Apache Spark

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789349108
Length 154 pages
Edition 1st Edition
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Authors (2):
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Akash Grade Akash Grade
Author Profile Icon Akash Grade
Akash Grade
LinkedIn
Akash Grade is a data engineer living in New Delhi, India. Akash graduated with a BSc in computer science from the University of Delhi in 2011, and later earned an MSc in software engineering from BITS Pilani. He spends most of his time designing highly scalable data pipeline using big-data solutions such as Apache Spark, Hive, and Kafka. Akash is also a Databricks-certified Spark developer. He has been working on Apache Spark for the last five years, and enjoys writing applications in Python, Go, and SQL.
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Shrey Mehrotra Shrey Mehrotra
Author Profile Icon Shrey Mehrotra
Shrey Mehrotra
Shrey Mehrotra has over 8 years of IT experience and, for the past 6 years, has been designing the architecture of cloud and big-data solutions for the finance, media, and governance sectors. Having worked on research and development with big-data labs and been part of Risk Technologies, he has gained insights into Hadoop, with a focus on Spark, HBase, and Hive. His technical strengths also include Elasticsearch, Kafka, Java, YARN, Sqoop, and Flume. He likes spending time performing research and development on different big-data technologies. He is the coauthor of the books Learning YARN and Hive Cookbook, a certified Hadoop developer, and he has also written various technical papers.
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to Apache Spark FREE CHAPTER 2. Apache Spark Installation 3. Spark RDD 4. Spark DataFrame and Dataset 5. Spark Architecture and Application Execution Flow 6. Spark SQL 7. Spark Streaming, Machine Learning, and Graph Analysis 8. Spark Optimizations 9. Other Books You May Enjoy

Spark DataFrame and Dataset

In the previous chapter, we learned about RDD concepts and APIs. In this chapter, we will explore DataFrame APIs, which are abstractions over RDDs, and also discuss the dataset APIs that come with Spark 2.0 to provide various optimizations over DataFrames.

The following topics will be covered in this chapter:

  • DataFrames
  • Datasets
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Authors (2)

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Profile icon Shrey Mehrotra
Shrey Mehrotra
Shrey Mehrotra has over 8 years of IT experience and, for the past 6 years, has been designing the architecture of cloud and big-data solutions for the finance, media, and governance sectors. Having worked on research and development with big-data labs and been part of Risk Technologies, he has gained insights into Hadoop, with a focus on Spark, HBase, and Hive. His technical strengths also include Elasticsearch, Kafka, Java, YARN, Sqoop, and Flume. He likes spending time performing research and development on different big-data technologies. He is the coauthor of the books Learning YARN and Hive Cookbook, a certified Hadoop developer, and he has also written various technical papers.
Read more
See other products by Shrey Mehrotra
Profile icon Grade
Grade
Akash Grade is a data engineer living in New Delhi, India. Akash graduated with a BSc in computer science from the University of Delhi in 2011, and later earned an MSc in software engineering from BITS Pilani. He spends most of his time designing highly scalable data pipeline using big-data solutions such as Apache Spark, Hive, and Kafka. Akash is also a Databricks-certified Spark developer. He has been working on Apache Spark for the last five years, and enjoys writing applications in Python, Go, and SQL.
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See other products by Grade
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