Big Data Analytics: A Hands-On Approach

Big Data Analytics: A Hands-On Approach book is really good with detailed explanation of theory and practical impact. Very good book to read who wants to gain knowledge on Bigdata analytics.

Its good book for beginners

Overall a great book with good practical examples of Big data tools. Cover spark, Kafka and other frameworks on Big data. Also, cover few examples on machine learning algorithms with python.

Good

Excellent book to start learning about big data technologies, algorithms, tools and frameworks. Includes a lot of practical code examples in Python.

Good book on Big data analytics covering introductory concepts, tools and some of the machine learning algorithms with examples. For a first course on Big Data Analytics, this book adds lot of value to the course as a reference book.

Libro consigliato dal mio professore per approfondire i concetti sui Big Data.Molto completo ed utile per avere una panoramica generale sull'argomento

The book was used but it looks like new.A very good coverage of the subject. I would highly recommend this book for anyone interested in knowing more about Big Data.This is a good book in terms of "foundation".

I love this book. It is so much of a straightforward "how-to". One of the better intro books out there.

Implementation sections almost always feature code that does not work or even almost work. Assumes a great deal of knowledge or often "skips ahead" using language early on that isn't given meaning until much later.

There are very few comprehensive and current books about the broadrange of Big Data Analytics which are suitable for use as universitytextbooks for the the subject. Bahga and Madisetti's book is anexception, introducing the characteristics of Big Data projects,surveying modern analytic concepts and methods, and including avariety of illustrative and relevant case studies. While not goinginto great depth on the use of specific software tools, the bookdoes provide enough introductory guidance for further explorationof such topics as Hadoop, NoSQL, and many of their related toolsfor data management and analysis. Additional tool-specific referencescan then be used to augment their coverage.The authors' explanations are clear with numerous helpful diagramsand code examples using Python and other programs. They includebrief instructions for using online Big Data services such as thoseprovided by Hortonworks, Cloudera, Amazon Web Services, and Azure.As a textbook, this reference is quite suitable for introductoryData Analytics courses, as it presents the "big picture" of thisemerging and rapidly evolving area of technology. The instructors'website for the book does not yet include sample presentations norstudent exercises, but these are easily created from the basematerial.The authors clearly prefer Python and related tools over R foranalysis, and there is little mention of the latter in the book.However, that too is easily augmented by instructors who wish toinclude R in their courses for students with less programmingexperience.