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Mastering Hadoop 3: Big data processing at scale to unlock unique business insights

Singh

Wong

Kumar

NZ$49.99 ~~NZ$71.99~~

5 (1 Ratings)

eBook Feb 2019 544 pages 1st Edition

eBook

NZ$49.99 ~~NZ$71.99~~

Singh

Wong

Kumar

NZ$49.99 ~~NZ$71.99~~

5 (1 Ratings)

eBook Feb 2019 544 pages 1st Edition

eBook

NZ$49.99 ~~NZ$71.99~~

NZ$49.99 ~~NZ$71.99~~

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Key benefits

Get to grips with the newly introduced features and capabilities of Hadoop 3
Crunch and process data using MapReduce, YARN, and a host of tools within the Hadoop ecosystem
Sharpen your Hadoop skills with real-world case studies and code

Description

Apache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency. With this guide, you’ll understand advanced concepts of the Hadoop ecosystem tool. You’ll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You’ll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you’ll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals. By the end of this book, you’ll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you’ll be equipped to tackle a range of real-world problems in data pipelines.

Who is this book for?

If you want to become a big data professional by mastering the advanced concepts of Hadoop, this book is for you. You’ll also find this book useful if you’re a Hadoop professional looking to strengthen your knowledge of the Hadoop ecosystem. Fundamental knowledge of the Java programming language and basics of Hadoop is necessary to get started with this book.

What you will learn

Gain an in-depth understanding of distributed computing using Hadoop 3
Develop enterprise-grade applications using Apache Spark, Flink, and more
Build scalable and high-performance Hadoop data pipelines with security, monitoring, and data governance
Explore batch data processing patterns and how to model data in Hadoop
Master best practices for enterprises using, or planning to use, Hadoop 3 as a data platform
Understand security aspects of Hadoop, including authorization and authentication

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Product Details

Publication date : Feb 28, 2019

Length: 544 pages

Edition : 1st

Language : English

ISBN-13 : 9781788628327

Vendor :

Apache

Category :

Data

Languages :

Java

Concepts :

Data Processing

Tools :

Hadoop

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20 Chapters

Section 1: Introduction to Hadoop 3

Journey to Hadoop 3

Hadoop origins and Timelines

Overview of Hadoop 3 and its features

Deep Dive into the Hadoop Distributed File System

Technical requirements

Defining HDFS

Deep dive into the HDFS architecture

NameNode internals

DataNode internals

Quorum Journal Manager (QJM)

HDFS high availability in Hadoop 3.x

Data management

HDFS reads and writes

Managing disk-skewed data in Hadoop 3.x

Lazy persist writes in HDFS

Erasure encoding in Hadoop 3.x

HDFS common interfaces

HDFS command reference

Points to remember

Summary

YARN Resource Management in Hadoop

Architecture

Introduction to YARN job scheduling

FIFO scheduler

Capacity scheduler

Fair scheduler

Resource Manager high availability

Node labels

YARN Timeline server in Hadoop 3.x

Opportunistic containers in Hadoop 3.x

Docker containers in YARN

YARN REST APIs

YARN command reference

Summary

Internals of MapReduce

Technical requirements

Deep dive into the Hadoop MapReduce framework

YARN and MapReduce

MapReduce workflow in the Hadoop framework

Common MapReduce patterns

MapReduce use case

Optimizing MapReduce

Summary

Section 2: Hadoop Ecosystem

SQL on Hadoop

Technical requirements

Presto – introduction

Hive

Impala

Summary

Real-Time Processing Engines

Technical requirements

Widely Used Hadoop Ecosystem Components

Technical requirements

Section 3: Hadoop in the Real World

Designing Applications in Hadoop

Technical requirements

Common batch processing pattern

Airflow for orchestration

Data governance

Summary

Real-Time Stream Processing in Hadoop

Technical requirements

What are streaming datasets?

Stream data ingestion

Common stream data processing patterns

Streaming design considerations

Micro-batch processing case study

Real-time processing case study

Summary

Machine Learning in Hadoop

Technical requirements

Machine learning steps

Common machine learning challenges

Spark machine learning

Hadoop and R

Mahout

Machine learning case study in Spark

Summary

Hadoop in the Cloud

Technical requirements

Logical view of Hadoop in the cloud

Network

Managing resources

Data pipelines

High availability (HA)

Summary

Hadoop Cluster Profiling

Introduction to benchmarking and profiling

Section 4: Securing Hadoop

Who Can Do What in Hadoop

Hadoop security pillars

System security

Kerberos authentication

User authorization

List of security features that have been worked upon in Hadoop 3.0

Summary

Network and Data Security

Securing Hadoop networks

Monitoring Hadoop

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Customer reviews

Rating distribution

(1 Ratings)

5 star 100%

4 star 0%

3 star 0%

2 star 0%

1 star 0%

SuchRave Mar 14, 2019

Its a wonderful book, giving details about overall ecosystem very well. Kudos!

Amazon Verified review

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About the 3 authors

Singh

Contacted on 12/01/18 by Davis Anto

See other products by Singh

Wong

Dr. Timothy Wong is a 30 years IT veteran. He holds a PhD in networking from University of Manchester Institute of Science and Technology, UK. He has deep experience in wireless and wireline networking, digital a/v, software development, sales and consulting. He frequently works with telcos, banks, governments and enterprises. He is also a professor on Big Data, Wireless and IoT at Humber College, Toronto, Canada. As an entrepreneur, he co-founded a number of companies in the past 20 years. He infuses business and technical knowledge to pursue successes.

See other products by Wong

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.

See other products by Kumar

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Mastering Hadoop 3: Big data processing at scale to unlock unique business insights

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