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The Prentice Hall Service Technology Series from Thomas Erl
Big Data Fundamentals: Concepts, Drivers & Techniques
“This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 “V” characteristics of datasets in Big Data volume, velocity, variety, veracity, and value Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning
- GenresProgrammingTechnology
238 pages, Kindle Edition
First published December 20, 2015
26 people are currently reading
169 people want to read
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Displaying 1 - 9 of 9 reviews
July 21, 2018
Pros:
The book is good for someone to get an overview of the topic, technologies and flow involved in BIG Data analytics. The good thing is that it gives you the understanding of linkage between different technologies and their application on concerned step of analytics. This hence becomes a great place to start. The depth is not provided, but that can be attained from online articles. Link, flow is important and I found this book as a decent compilation. There is one case study that follows along the chapters, giving you an insight of real application of things being talked about.
Cons:
The topics are just touched superficially. So don't expect too much.
The diagrams are useless. Utterly useless. Also, many diagrams are unnecessarily printed multiple times for no purpose. Its a nice good read only if the price you are purchasing it at doesn't look hefty. Research your options.
Happy reading
The book is good for someone to get an overview of the topic, technologies and flow involved in BIG Data analytics. The good thing is that it gives you the understanding of linkage between different technologies and their application on concerned step of analytics. This hence becomes a great place to start. The depth is not provided, but that can be attained from online articles. Link, flow is important and I found this book as a decent compilation. There is one case study that follows along the chapters, giving you an insight of real application of things being talked about.
Cons:
The topics are just touched superficially. So don't expect too much.
The diagrams are useless. Utterly useless. Also, many diagrams are unnecessarily printed multiple times for no purpose. Its a nice good read only if the price you are purchasing it at doesn't look hefty. Research your options.
Happy reading
June 21, 2022
Good bird’s eye view. Don’t have too much expectations and you won’t be disappointed.
June 25, 2022
This is a topical book on basic processes to understand WHAT to do with Big Data and high level HOW to analyze the Big Data landscape.
One of the often neglected parts of data analytics tends to be of an intentional approach to working out the limitations and trade-offs that accompany available choices on analytical approaches, types of storage and processing methodologies etc. This book provides helpful summaries of various technologies & applications and understanding of their pros & cons. It is presented in a readable easy to understand way and should encourage readers to explore further
Expect to quickly bring yourself up-to-speed on the key topics and issues in Big data, and then based on interest do more in-depth of exploration on the various topics overviewed.
One of the often neglected parts of data analytics tends to be of an intentional approach to working out the limitations and trade-offs that accompany available choices on analytical approaches, types of storage and processing methodologies etc. This book provides helpful summaries of various technologies & applications and understanding of their pros & cons. It is presented in a readable easy to understand way and should encourage readers to explore further
Expect to quickly bring yourself up-to-speed on the key topics and issues in Big data, and then based on interest do more in-depth of exploration on the various topics overviewed.
February 26, 2021
It is a beginners book and superficial on all topics, so don't expect reading it and becoming a Data Scientist. That said, it is indeed a nice introduction to Big Data covering a decent amount of basic terminology, and certainly helps to create some foundational knowledge.
November 16, 2023
Muy buen libro académico, donde dan guía de todo tipo de conceptos referentes al big data y de cómo se puede almacenar la información en las diferentes bases de datos con sus patrones de almacenamiento.
July 11, 2019
concise and precise. good entry point for big data ecosystem. I have taken advantage of this book for my introductory lecture on big data analytics course.
March 23, 2024
Recommend for academic purpose and those who wants to start learning about big data.
April 21, 2016
Modern business systems accumulate huge amounts of data from diverse application domains. Big Data is an interdisciplinary branch of computing which is concerned with various aspects of the techniques and technologies involved in exploiting these very large, disparate data sources.
The eight chapters of this book are organised into two sections which together provide a high-level overview of the Big Data landscape.
The first section is concerned with Big Data in the business. In this section, the principal concepts and terminology of Big Data are introduced along with high-level discussion of the kinds of problems that Big Data can help to solve and the general approaches to these solutions.
The section outlines a Big Data analytics lifecycle with which businesses may begin to incorporate Big Data into their processes in order to derive value from their data sets. The section closes with a brief review of the roles and limitations of the typical data processing components that are present within modern businesses and identifies where Big Data fits into this.
Having focussed in the first section on the “what?” and “why?” of Big Data, the second section goes on to consider the “how?”, by discussing the principal concepts and technologies that underpin Big Data implementations. As the book describes, the volume and variety of data involved in Big Data projects has wide ranging implications for data storage, processing and analysis.
The book outlines approaches to distributed data storage, the limitations and trade-offs that have to be made, as well as describing techniques for storing non-relational data using alternative database systems such as NoSQL and Graph databases. The book outlines the Map/Reduce approach to distributed batch processing and summarises common machine learning concepts and data visualisation techniques. The authors provide helpful summaries of each technology, giving the types of applications to which each is suited to as well as those to which they are not.
This is a brief, informative and very readable introduction to Big Data which enables the reader to quickly bring themselves up-to-speed on the key topics and issues as well as serving as a basis for further exploration of topics of interest. The book should attract a broad readership of business users and technologists.
Review by Patrick Hill MBCS CEng CITP
Originally posted: http://www.bcs.org/content/conWebDoc/...
The eight chapters of this book are organised into two sections which together provide a high-level overview of the Big Data landscape.
The first section is concerned with Big Data in the business. In this section, the principal concepts and terminology of Big Data are introduced along with high-level discussion of the kinds of problems that Big Data can help to solve and the general approaches to these solutions.
The section outlines a Big Data analytics lifecycle with which businesses may begin to incorporate Big Data into their processes in order to derive value from their data sets. The section closes with a brief review of the roles and limitations of the typical data processing components that are present within modern businesses and identifies where Big Data fits into this.
Having focussed in the first section on the “what?” and “why?” of Big Data, the second section goes on to consider the “how?”, by discussing the principal concepts and technologies that underpin Big Data implementations. As the book describes, the volume and variety of data involved in Big Data projects has wide ranging implications for data storage, processing and analysis.
The book outlines approaches to distributed data storage, the limitations and trade-offs that have to be made, as well as describing techniques for storing non-relational data using alternative database systems such as NoSQL and Graph databases. The book outlines the Map/Reduce approach to distributed batch processing and summarises common machine learning concepts and data visualisation techniques. The authors provide helpful summaries of each technology, giving the types of applications to which each is suited to as well as those to which they are not.
This is a brief, informative and very readable introduction to Big Data which enables the reader to quickly bring themselves up-to-speed on the key topics and issues as well as serving as a basis for further exploration of topics of interest. The book should attract a broad readership of business users and technologists.
Review by Patrick Hill MBCS CEng CITP
Originally posted: http://www.bcs.org/content/conWebDoc/...
July 18, 2016
Decent technical overview of Big Data, but a bit over my head personally.
Displaying 1 - 9 of 9 reviews