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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

Hadoop Design Consideration

Big data does not necessarily mean huge data. If a dataset is small, it's very easy to analyze it. We can load it on to an Excel spreadsheet and do the required calculations. But, as the volume of data gets bigger, we have to find other alternatives to process it. We may have to load it to an RDMBS table and run a SQL query to find the trend and patterns on the given structure. Further, if the dataset format changes to something like email, then loading to RDBMS becomes a huge challenge. To add more complexity to it, if the data speed changes to something like real time, it becomes almost impossible to analyze the given dataset with traditional RDBMS-based tools. In the modern world, the term big data can be expressed using the five most famous Vs. Following is the explanation of each V in a nutshell.

In this chapter, we will cover the following...

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