BPB Online LLP
Data Processing and Modeling with Hadoop
Vinicius Aquino do Vale
Data Processing and Modeling with Hadoop
US$ 19.95
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Description
Contents
Reviews

Understand data in a simple way using a data lake.

Key Features
● In-depth practical demonstration of Hadoop/Yarn concepts with numerous examples.
● Includes graphical illustrations and visual explanations for Hadoop commands and parameters.
● Includes details of dimensional modeling and Data Vault modeling.
● Includes details of how to create and define a structure to a data lake.

Description
The book 'Data Processing and Modeling with Hadoop' explains how a distributed system works and its benefits in the big data era in a straightforward and clear manner. After reading the book, you will be able to plan and organize projects involving a massive amount of data.

The book describes the standards and technologies that aid in data management and compares them to other technology business standards. The reader receives practical guidance on how to segregate and separate data into zones, as well as how to develop a model that can aid in data evolution. It discusses security and the measures that are utilized to reduce the impact of security. Self-service analytics, Data Lake, Data Vault 2.0, and Data Mesh are discussed in the book.

After reading this book, the reader will have a thorough understanding of how to structure a data lake, as well as the ability to plan, organize, and carry out the implementation of a data-driven business with full governance and security.

What you will learn
● Learn the basics of components to the Hadoop Ecosystem.
● Understand the structure, files, and zones of a Data Lake.
● Learn to implement the security part of the Hadoop Ecosystem.
● Learn to work with the Data Vault 2.0 modeling.
● Learn to develop a strategy to define good governance.
● Learn new tools to work with Data and Big Data

Who this book is for
This book caters to big data developers, technical specialists, consultants, and students who want to build good proficiency in big data. Knowing basic SQL concepts, modeling, and development would be good, although not mandatory.

Table of Contents
1. Understanding the Current Moment
2. Defining the Zones
3. The Importance of Modeling
4. Massive Parallel Processing
5. Doing ETL/ELT
6. A Little Governance
7. Talking About Security
8. What Are the Next Steps?

Language
English
ISBN
9789391392284
Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Reviewer
Acknowledgement
Preface
Errata
Table of Contents
1. Understanding the Current Moment
Introduction
Structure
Objectives
A little context
Why use it?
Solving problems
Hadoop ecosystem
Building the data lake
What does the data tell us?
Conclusion
Points to remember
Questions
Multiple choice questions
Answers
2. Defining the Zones
Introduction
Structure
Objectives
Why separate data into zones?
Transition zone
RAW zone
Trusted zone
Refined zone
Where to put my Sandbox
Conclusion
Points to remember
Questions
Multiple choice questions
Answers
3. The Importance of Modeling
Introduction
Structure
Objectives
Why should we model our environment?
Data Vault 2.0
How to plan modeling
Conclusion
Points to remember
Questions
Multiple choice questions
Answers
4. Massive Parallel Processing
Introduction
Structure
Objectives
How did we arrive and where did we arrive?
What is MapReduce?
MapReduce features
Introduction to Spark
Resource Manager – YARN
Introduction to Apache Tez
Conclusion
Points to remember
Questions
Multiple choice questions
Answers
5. Doing ETL/ELT
Introduction
Structure
Objectives
Transforming data into information
Identifying enemies
Main types of transformations
Planning the rollback
Why a data mart?
Feedback
Data lake and data warehouse secrets
Conclusion
Points to remember
Questions
Multiple choice questions
Answers
6. A Little Governance
Introduction
Structure
Objectives
Governing the data
Main difficulties
What methodologies and tools to use?
Defining a deployment roadmap
Conclusion
Points to remember
Questions
Multiple choice questions
Answers
7. Talking About Security
Introduction
Structure
Objectives
Need to worry about security
The main difficulties
Making identification, authorization, and authentication
The main tools
Defining a schedule
Conclusion
Points to remember
Questions
Multiple choice questions
Answers
8. What Are the Next Steps?
Introduction
Structure
Objectives
A new era
Separating a batch from real time
Defining the visualization tools
Machine learning
New tendencies
Conclusion
Questions
Multiple choice questions
Answers
Index
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