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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
Languages
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

Best practices Hadoop deployment

Following are some best practices to be followed for Hadoop deployment:

  • Start small: Like other software projects, an implementation Hadoop also involves risks and cost. It's always better to set up a small Hadoop cluster of four nodes. This small cluster can be set up as proof of concept (POC). Before using any Hadoop component, it can be added to the existing Hadoop POC cluster as proof of technology (POT). It allows the infrastructure and development team to understand big data project requirements. After successful completion of POC and POT, additional nodes can be added to the existing cluster.
  • Hadoop cluster monitoring: Proper monitoring of the NameNode and all DataNodes is required to understand the health of the cluster. It helps to take corrective actions in the event of node problems. If a service goes down, timely action can help...
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