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Data Lake for Enterprises

You're reading from Data Lake for Enterprises

Product type Book

Published in May 2017

Publisher Packt

ISBN-13 9781787281349

Pages 596 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Authors (3):

Vivek Mishra

Tomcy John

Pankaj Misra

View More author details

Table of Contents (23) Chapters

Title Page

Credits

Foreword

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

Part 1 - Overview

Part 2 - Technical Building blocks of Data Lake

Part 3 - Bringing It All Together

1. Introduction to Data

2. Comprehensive Concepts of a Data Lake

3. Lambda Architecture as a Pattern for Data Lake

4. Applied Lambda for Data Lake

5. Data Acquisition of Batch Data using Apache Sqoop

6. Data Acquisition of Stream Data using Apache Flume

7. Messaging Layer using Apache Kafka

8. Data Processing using Apache Flink

9. Data Store Using Apache Hadoop

10. Indexed Data Store using Elasticsearch

11. Data Lake Components Working Together

12. Data Lake Use Case Suggestions

Why Hadoop?

For me, the question Why Hadoop? is not really a question. In the industry as of now, for big data Apache Hadoop is indispensable. There are alternatives, but most of them work in conjunction with Hadoop. Listed here are some of the prominent reasons why Hadoop is technology of choice for the technical capability that we are looking for in a Data Lake implementation:

It can handle high volumes of structured, semi-structured, and unstructured data with ease.
It is less costly to implement as it can start off using commodity hardware and scale according to organization all requirement.
It has the ever growing Apache community to support it with frequent releases, releasing bug fixes and enhancements alike. Hadoop, as you know, has two core layers, namely the compute and data (HDFS) layers. The compute layer adds new frameworks and libraries, such as Pig and Hive, on top of the Hadoop ecosystem, making Hadoop all the more relevant for many use cases.
The library of Hadoop itself is...

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Authors (3)

Vivek Mishra

Vivek Mishra is an IT professional with more than nine years of experience in various technologies like Java, J2ee, Hibernate, SCA4J, Mule, Spring, Cassandra, HBase, MongoDB, REDIS, Hive, Hadoop. He has been a contributor for open source like Apache Cassandra and lead committer for Kundera(JPA 2.0 compliant Object-Datastore Mapping Library for NoSQL Datastores like Cassandra, HBase, MongoDB and REDIS). Mr Mishra in his previous experience has enjoyed long lasting partnership with most recognizable names in SCM, Banking and finance industries, employing industry standard full software life cycle methodologies Agile and SCRUM. He is currently employed with Impetus infotech pvt. ltd. He has undertaken speaking engagements in cloud camp and Nasscom Big data seminar and is an active blogger and can be followed at mevivs.wordpress.com

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Tomcy John

Tomcy John lives in Dubai (United Arab Emirates), hailing from Kerala (India), and is an enterprise Java specialist with a degree in Engineering (B Tech) and over 14 years of experience in several industries. He's currently working as principal architect at Emirates Group IT, in their core architecture team. Prior to this, he worked with Oracle Corporation and Ernst & Young. His main specialization is in building enterprise-grade applications and he acts as chief mentor and evangelist to facilitate incorporating new technologies as corporate standards in the organization. Outside of his work, Tomcy works very closely with young developers and engineers as mentors and speaks at various forums as a technical evangelist on many topics ranging from web and middleware all the way to various persistence stores.

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Pankaj Misra

Pankaj Misra has been a technology evangelist, holding a bachelor's degree in engineering, with over 16 years of experience across multiple business domains and technologies. He has been working with Emirates Group IT since 2015, and has worked with various other organizations in the past. He specializes in architecting and building multi-stack solutions and implementations. He has also been a speaker at technology forums in India and has built products with scale-out architecture that support high-volume, near-real-time data processing and near-real-time analytics.

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