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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

Understanding data structure principles

Let's go through some important data architecture principles:

  • Data is an asset to an enterprise: Data has a measurable value. It provides some real value to the enterprise. In modern times, data is treated like real gold.
  • Data is shared enterprise-wide: Data is captured only once and then used and analyzed many times. Multiple users access the same data for different uses cases and requirements.
  • Data governance: Data is governed to ensure data quality.
  • Data management: Data needs to be managed to attain enterprise objectives.
  • Data access: All users should have access to data.
  • Data security: Data should be properly secured and protected.
  • Data definition: Each attribute of the data needs to be consistently defined enterprise-wide.

Now that we know the basics of big data and its principles, let's get into some real action.

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