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Modern Big Data Processing with Hadoop

You're reading from   Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Length 394 pages
Edition 1st Edition
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Concepts
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Authors (3):
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 R Patil R Patil
Author Profile Icon R Patil
R Patil
 Shindgikar Shindgikar
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Shindgikar
 Kumar Kumar
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Kumar
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Toc

Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Enterprise Data Architecture Principles 2. Hadoop Life Cycle Management FREE CHAPTER 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 Index

Summary


In this chapter, we learned about the different data life cycle stages, including when data is created, shared, maintained, archived, retained, and deleted.

This chapter gave you a detailed understanding of how big data is managed, considering the fact that it is either unstructured or semi-structured and it has a fast arrival rate and large volume.

As the complexity of the infrastructure that generates and uses data in business organizations has increased drastically, it has become imperative to secure your data properly. This chapter further covered data security tools, such as Apache Ranger, and patterns to help us learn how to have control over the access patterns of data.

In the next chapter, we will take a look at Hadoop installation, its architecture and key components.

You have been reading a chapter from
Modern Big Data Processing with Hadoop
Published in: Mar 2018
Publisher: Packt
ISBN-13: 9781787122765
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