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Learning Apache Spark 2

You're reading from  Learning Apache Spark 2

Product type Book
Published in Mar 2017
Publisher Packt
ISBN-13 9781785885136
Pages 356 pages
Edition 1st Edition
Languages
Concepts

Table of Contents (18) Chapters

Learning Apache Spark 2
Credits
About the Author
About the Reviewers
www.packtpub.com
Customer Feedback
Preface
1. Architecture and Installation 2. Transformations and Actions with Spark RDDs 3. ETL with Spark 4. Spark SQL 5. Spark Streaming 6. Machine Learning with Spark 7. GraphX 8. Operating in Clustered Mode 9. Building a Recommendation System 10. Customer Churn Prediction 1. Theres More with Spark

Working with Hive

Hive is a data warehousing infrastructure based on Hadoop. Hive provides SQL-like capabilities to work with data on Hadoop. Hadoop, during its infancy was limited to MapReduce as a computer platform, which was a very engineer-centric programming paradigm. Engineers at Facebook in 2008 were writing fairly complex Map-Reduce jobs, but realised that it would not be scalable and it would be difficult to get the best value from the available talent. Having a team that could write Map-Reduce Jobs, and be called upon was considered a poor strategy and hence the team decided to bring SQL to Hadoop (Hive) due for two major reasons:

  • An SQL-based declarative language while allowing engineers to plug their own scripts and programs when SQL did not suffice.
  • Centralized metadata about all data (Hadoop based datasets) in the organization, to create a data-driven organization.

Spark supports reading and writing data stored in Apache Hive. You would need to configure Hive with Apache Spark...

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