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Simplifying Big Data in 7 Chapters
Paperback
Series: Big Data, Book 1
Publisher: Independently Published
Published: Feb 15 2025
Pages: 134
Weight: 0.42
Height: 0.29 Width: 6.00 Depth: 9.00
Language: English
- 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. About the author:
Prof. Marcão, Master in Information Technology and author of more than 200 books, is a technology thinker who has been working in information architecture and attribute engineering since the 1980s. His most significant contribution to the field lies in the development of innovative methodologies for Big Data and Data Warehouse, including data modeling standards, data warehouse, data model validation and management, as well as data mart development.
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