Pearson Big Data Fundamentals: Concepts, Drivers & Techniques

Completely useless. Half of the book is on traditional DBMS. Nothing you can't already find on the web regarding Big Data. NoSQL and HDFS are topics that were not dived in deep enough, barely covering few pages in the book. A real disappointment.

Es un libro que en 60 páginas podría estar escrito. Las primeras 90 páginas son repetitivas. Las restantes aunque cuentan algo adolecen del mismo problema. El caso de estudio o ejemplo es patético y repetitivo. No oporta nada.Sólo son conceptos expuestos muy someramente. Ayuda a saber que terminos se usan en inglés pero poco mas. Si quieres tener una idea pero sin entrar en cuestiones técnicas es un libro útil aunque repetitivo. El inglés que usan es sencillo de entenderPor mi parte esperaba mas. Entre otras cosas mas de una editorial como Prentice Hall de la cual tengo buenos libros técnicos, pero que en ciertas partes del libro permiten usar el término a definir como parte de la definición.

easy to read and understand basic concepts about Big Data

Book is good all concepts are very well explained and language is also very easy

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.