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You're reading from  Apache Spark Quick Start Guide

Product typeBook
Published inJan 2019
Reading LevelIntermediate
PublisherPackt
ISBN-139781789349108
Edition1st Edition
Languages
Scala
Tools
Apache Spark
Concepts
Data Analysis
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Authors (2):
Shrey Mehrotra
Shrey Mehrotra
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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 about Shrey Mehrotra

Akash Grade
Akash Grade
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Akash 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.
Read more about Akash Grade

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DataFrames

As we already mentioned, DataFrame APIs are abstractions of RDD APIs. DataFrames are distributed collections of data that are organized in the form of rows and columns. In other words, DataFrames provide APIs to efficiently process structured data that's available in different sources. The sources could be an RDD, different types of files in a filesystem, any RDBMS, or Hive tables.

The features of DataFrames are as follows:

  • DataFrames can process data that's available in different formats, such as CSV, AVRO, and JSON, or stored in any storage media, such as Hive, HDFS, and RDBMS
  • DataFrames can process data volumes from kilobytes to petabytes
  • Use the Spark-SQL query optimizer to process data in a distributed and optimized manner
  • Support for APIs in multiple languages, including Java, Scala, Python, and R
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Apache Spark Quick Start Guide
Published in: Jan 2019Publisher: PacktISBN-13: 9781789349108
© 2019 Packt Publishing Limited All Rights Reserved

Authors (2)

author image
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 about Shrey Mehrotra

author image
Akash 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.
Read more about Akash Grade

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