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Modern Big Data Processing with Hadoop

You're reading from  Modern Big Data Processing with Hadoop

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Pages 394 pages
Edition 1st Edition
Languages
Concepts
Authors (3):
V Naresh Kumar V Naresh Kumar
Profile icon V Naresh Kumar
Manoj R Patil Manoj R Patil
Profile icon Manoj R Patil
Prashant Shindgikar Prashant Shindgikar
Profile icon Prashant Shindgikar
View More author details

Table of Contents (12) Chapters

Preface 1. Enterprise Data Architecture Principles 2. Hadoop Life Cycle Management 3. Hadoop Design Consideration 4. Data Movement Techniques 5. Data Modeling in Hadoop 6. Designing Real-Time Streaming Data Pipelines 7. Large-Scale Data Processing Frameworks 8. Building Enterprise Search Platform 9. Designing Data Visualization Solutions 10. Developing Applications Using the Cloud 11. Production Hadoop Cluster Deployment

Data Modeling in Hadoop

So far, we've learned how to create a Hadoop cluster and how to load data into it. In the previous chapter, we learned about various data ingestion tools and techniques. As we know by now, there are various open source tools available in the market, but there is a single silver bullet tool that can take on all our use cases. Each data ingestion tool has certain unique features; they can prove to be very productive and useful in typical use cases. For example, Sqoop is more useful when used to import and export Hadoop data from and to an RDBMS.

In this chapter, we will learn how to store and model data in Hadoop clusters. Like data ingestion tools, there are various data stores available. These data stores support different data models—that is, columnar data storage, key value pairs, and so on; and they support various file formats, such as ORC...

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