SIMPLIFYING BIG DATA IN 7 CHAPTERS

Throughout the text, you will discover how Big Data is transforming the way companies operate and how we can extract valuable insights from large volumes of data. From the theoretical underpinnings to the best practices and tools available, this book is an essential guide for beginners and professionals who want to excel in this ever-evolving field.
With a practical approach, I share valuable tips for collecting, storing, processing and analyzing data. Additionally, it discusses the ethical and security challenges that come with handling sensitive information and how to properly address them.
"Simplifying Big Data into 7 Chapters" is a treasure trove of knowledge for entrepreneurs, managers, students, and enthusiasts who want to unlock the secrets of Big Data and use it as a competitive advantage in business.
Written in an accessible and engaging way, this book is an invitation to explore a universe of possibilities. Whether you're new to the field of Big Data or a seasoned professional looking for a refresher, this book is sure to broaden your horizons and provide you with the tools you need to gain valuable insights and turn data into tangible results.
In this book I present:
The fundamental concepts of Big Data and what makes it different from previous forms of data science and analytics.
The business motivations and drivers driving big data adoption, from operational improvements to innovation.
The 5 Vs of Datasets in Big Data Environments: Volume, Velocity, Variety, Veracity, and Value
The comparative analysis between Data Warehouse and Big Data and the possibilities of working with hybrid platforms.
The techniques and characteristics of working with structured, unstructured, semi-structured and metadata data.
Applications in the various business sectors and public administration.
How to work with Cloud Computing, virtualization, Analytics and streaming in a Big Data context.
NoSQL, columnar databases, spatial databases, and other technologies to meet the distinct data processing requirements of Big Data.
The components of the Big Data framework, such as MapReduce and Hadoop.
The technological and informational architecture of the Big Data proposal.
Aspects to consider to implement Big Data in your company.
Issues of security, governance, ethics and impacts on society.
Who is this book for:
This book can be used as a textbook or complement to the disciplines that intersect with Big Data, Data Science, data processing and Internet information analysis.In addition, it can be used by professionals in information technology, data administration, data architecture, attribute engineering, business analysts and systems analysts involved in data analysis projects.

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