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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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Manoj R Patil Manoj R Patil
Author Profile Icon Manoj R Patil
Manoj R Patil
Prashant Shindgikar Prashant Shindgikar
Author Profile Icon Prashant Shindgikar
Prashant Shindgikar
V Naresh Kumar V Naresh Kumar
Author Profile Icon V Naresh Kumar
V Naresh Kumar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Enterprise Data Architecture Principles FREE CHAPTER 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

Apache HBase

We have just learned about Hive, which is a database where users can access data using SQL commands. But there are certain databases where users cannot use SQL commands. Those databases are known as NoSQL data stores. HBase is a NoSQL database. So, what is actually meant by NoSQL? NoSQL means not only SQL. In NoSQL data stores like HBase, the main features of RDBMS, such as validation and consistency, are relaxed. Also, another important difference between RDBMS or SQL databases and NoSQL databases is schema on write versus schema on read. In schema on write, the data is validated at the time of writing to the table, whereas schema on read supports validation of data at the time of reading it. In this way, NoSQL data stores support storage of huge data velocity due to the relaxation of basic data validation at the time of writing data. There are about 150 NoSQL data...

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