---
product_id: 34860174
title: "Big Data Fundamentals: Concepts, Drivers & Techniques (The Pearson Service Technology Series from Thomas Erl)"
price: "$120.78"
currency: USD
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reviews_count: 12
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---

# Big Data Fundamentals: Concepts, Drivers & Techniques (The Pearson Service Technology Series from Thomas Erl)

**Price:** $120.78
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## Description

Big Data Fundamentals: Concepts, Drivers & Techniques (The Pearson Service Technology Series from Thomas Erl): 9780134291079: Computer Science Books @ desertcart.com

Review: A must read in Big Data - From the beginning well explained concepts and in every chapter the connections of concepts and the case study. Definitely a book to buy to understand in which case you could use: MongoDB, Cassandra or CouchDB
Review: Decent Big Data Book Falls Flat In A Few Areas - This book is divided into two parts with the first part introducing concepts about Big Data, and the second part discussing implementations of Big Data. I found the first part to use confusing wording, while the second part was written much better. I would give the first part 3-stars and the second part 5-stars if I was rating them individually. The first part of the book (Chapters 1-4) introduces a lot of acronyms and words that were glossed over. A lot of sentences were written in overly complicated language. To give an example on page 36 the discussion is about Business Process Management: "When BPM is combined with BPMSs that are intelligent, processes can be executed in a goal-driven manner. Goals are connected to process fragments that are dynamically chosen and assembled at run-time in alignment with the evaluation of the goals. When the combination of Big Data analytics results and goal-driven behavior are used together, process execution can become adaptive to the marketplace and responsive to environmental conditions." I found wording like this to be bogged down in corporate mumbo-jumbo and had I difficulty understanding in a lot of places. Chapter three felt particularly lazy to me. The exact same diagram that took up 3/4th's of the page was used 10 seperate times in chapter without any variation to the diagram (see the attached photo to get an idea). It shows a nine-step process, and for each step the diagram is shown without even highlighting the step we are on. Luckily the second part redeems itself. MapReduce, different NoSQL databases, analytic techniques, and storage techniques were described well here. The second part of the book gave much clearer and more concrete examples. The writing was much better in the second part of the book. This led me to believe the parts were written mostly seperately by the authors. Linking the chapters together is an insurance company called ETI. This is used as a case study at the end of each chapter. I felt the choice of an aging insurance company to be uninspiring for a big data solution. The analysis was oversimplified in these sections. For example, the authors might say something like the engineers at ETI are unfamiliar with CAP Theorem so they may need additional training. Or in one part, they said the company chose to go with a NoSQL database, but they do not mention what type of NoSQL database. I also noticed the writing in the second part to be better for these sections as well. Overall this book does a decent job of conveying the concepts for big data. It is heavily geared towards a corporate environment as there is almost zero talk of implementating a Big Data solution on your own. I felt it covered the topic pretty well though, but would have like to see the authors discuss specific technologies more, rather than gearing the book towards getting certified in Big Data.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #1,114,958 in Books ( See Top 100 in Books ) #159 in Data Warehousing (Books) #296 in Database Storage & Design #327 in Data Mining (Books) |
| Customer Reviews | 4.1 4.1 out of 5 stars (72) |
| Dimensions  | 0.6 x 9 x 6.9 inches |
| Edition  | 1st |
| ISBN-10  | 0134291077 |
| ISBN-13  | 978-0134291079 |
| Item Weight  | 13.4 ounces |
| Language  | English |
| Part of series  | The Pearson Service Technology Series from Thomas Erl |
| Print length  | 240 pages |
| Publication date  | January 5, 2016 |
| Publisher  | Pearson |

## Images

![Big Data Fundamentals: Concepts, Drivers & Techniques (The Pearson Service Technology Series from Thomas Erl) - Image 1](https://m.media-amazon.com/images/I/91gKuxwI6aL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ A must read in Big Data
*by E***R on January 22, 2017*

From the beginning well explained concepts and in every chapter the connections of concepts and the case study. Definitely a book to buy to understand in which case you could use: MongoDB, Cassandra or CouchDB

### ⭐⭐⭐⭐ Decent Big Data Book Falls Flat In A Few Areas
*by W***S on April 25, 2016*

This book is divided into two parts with the first part introducing concepts about Big Data, and the second part discussing implementations of Big Data. I found the first part to use confusing wording, while the second part was written much better. I would give the first part 3-stars and the second part 5-stars if I was rating them individually. The first part of the book (Chapters 1-4) introduces a lot of acronyms and words that were glossed over. A lot of sentences were written in overly complicated language. To give an example on page 36 the discussion is about Business Process Management: "When BPM is combined with BPMSs that are intelligent, processes can be executed in a goal-driven manner. Goals are connected to process fragments that are dynamically chosen and assembled at run-time in alignment with the evaluation of the goals. When the combination of Big Data analytics results and goal-driven behavior are used together, process execution can become adaptive to the marketplace and responsive to environmental conditions." I found wording like this to be bogged down in corporate mumbo-jumbo and had I difficulty understanding in a lot of places. Chapter three felt particularly lazy to me. The exact same diagram that took up 3/4th's of the page was used 10 seperate times in chapter without any variation to the diagram (see the attached photo to get an idea). It shows a nine-step process, and for each step the diagram is shown without even highlighting the step we are on. Luckily the second part redeems itself. MapReduce, different NoSQL databases, analytic techniques, and storage techniques were described well here. The second part of the book gave much clearer and more concrete examples. The writing was much better in the second part of the book. This led me to believe the parts were written mostly seperately by the authors. Linking the chapters together is an insurance company called ETI. This is used as a case study at the end of each chapter. I felt the choice of an aging insurance company to be uninspiring for a big data solution. The analysis was oversimplified in these sections. For example, the authors might say something like the engineers at ETI are unfamiliar with CAP Theorem so they may need additional training. Or in one part, they said the company chose to go with a NoSQL database, but they do not mention what type of NoSQL database. I also noticed the writing in the second part to be better for these sections as well. Overall this book does a decent job of conveying the concepts for big data. It is heavily geared towards a corporate environment as there is almost zero talk of implementating a Big Data solution on your own. I felt it covered the topic pretty well though, but would have like to see the authors discuss specific technologies more, rather than gearing the book towards getting certified in Big Data.

### ⭐⭐⭐⭐⭐ Five Stars
*by B***S on November 28, 2017*

Solid intro book on the subject.

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*Last updated: 2026-02-23*