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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 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

Large-Scale Data Processing Frameworks

As the volume and complexity of data sources are increasing, deriving value out of data is also becoming increasingly difficult. Ever since Hadoop was made, it has built a massively scalable filesystem, HDFS. It has adopted the MapReduce concepts from functional programming to approach the large-scale data processing challenges. As technology is constantly evolving to overcome the challenges posed by data mining, enterprises are also finding ways to embrace these changes to stay ahead.

In this chapter, we will focus on these data processing solutions:

  • MapReduce
  • Apache Spark
  • Spark SQL
  • Spark Streaming
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