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You're reading from Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights

Product type Paperback

Published in Mar 2018

Publisher Packt

ISBN-13 9781787122765

Length 394 pages

Edition 1st Edition

Languages

Processing

Tools

Apache Spark

Concepts

Big Data

Authors (3):

Manoj R Patil

Prashant Shindgikar

V Naresh Kumar

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Table of Contents (12) Chapters

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

Configuring HDFS high availability

Let's take a look at the changes brought about in Hadoop over time.

During Hadoop 1.x

Hadoop 1.x started with the architecture of a single NameNode. All DataNodes used to send their block reports to that single NameNode. There was a secondary NameNode in the architecture, but its sole responsibility was to merge all edits to FSImage. With this architecture, the NameNode became the single point of failure (SPOF). Since it has all the metadata of all the DataNodes of the Hadoop cluster, in the event of NameNode crash, the Hadoop cluster becomes unavailable till the next restart of NameNode repair. If the NameNode cannot be recovered, then all the data in all the DataNodes would be completely...

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Authors (3)

Shindgikar

Prashant Shindgikar is an accomplished big data Architect with over 20 years of experience in data analytics. He specializes in data innovation and resolving data challenges for major retail brands. He is a hands-on architect having an innovative approach to solving data problems. He provides thought leadership and pursues strategies for engagements with the senior executives on innovation in data processing and analytics. He presently works for a large USA-based retail company.

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R Patil

Manoj R Patil is the Chief Architect in Big Data at Compassites Software Solutions Pvt. Ltd. where he overlooks the overall platform architecture related to Big Data solutions, and he also has a hands-on contribution to some assignments. He has been working in the IT industry for the last 15 years. He started as a programmer and, on the way, acquired skills in architecting and designing solutions, managing projects keeping each stakeholder's interest in mind, and deploying and maintaining the solution on a cloud infrastructure. He has been working on the Pentaho-related stack for the last 5 years, providing solutions while working with employers and as a freelancer as well. Manoj has extensive experience in JavaEE, MySQL, various frameworks, and Business Intelligence, and is keen to pursue his interest in predictive analysis. He was also associated with TalentBeat, Inc. and Persistent Systems, and implemented interesting solutions in logistics, data masking, and data-intensive life sciences.

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Kumar

Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.

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