Using Spark with other big data technologies
935325
The book provides a super fast, short introduction to Spark in the first chapter and then jump straight into MLlib, Spark Streaming Spark SQL, GraphX, etc. in subsequent chapters.

A huge positive for this book is that it not only talks about Spark itself, but also covers using Spark with other big data technologies like Hadoop, Kafka, Titan, Neo4j, HBase, Cassandra, H2O, etc. More on this below.

True to the name, sure the book covers more than simple introductory Spark topics, but it concentrates on breath than depth. There is decent coverage and enough code examples for each topic, but what it lacks is depth. There is no "best practices" or "performance" or "watch out for" type discussions or any type of advanced code.

The MLlib chapter covers Naive Bayes, K-Means and Artificial Neural Networks (ANN). For each algorithm, the theory is very briefly introduced and then jumps right into detailed code walkthroughs.

The Spark Streaming chapter introduces Streaming briefly and jumps straight into using different streaming sources and code walkthroughs of how to use them. This chapter covers TCP streams, File streams, Flume and Kafka sources.

By now the pattern of the chapters should be evident. The next chapter on Spark SQL follows the same format covering different data source like, Text, Json, Parquet, Hive and covers DataFrame/SQL code examples.

GraphX is covered in the next two chapters. Integration of GraphX with Neo4j and Titan (both HBase and Cassandra backed store) is covered extensively.

Finally H2O integration and the Databricks Spark hosted offering is discussed.

I would definitely recommend this as the second Spark book after any Introductory Spark book (or Spark Documentation).
2801942
No problems connecting this to my TomTom Spark. Now I can get accurate data of how many calories I'm actually burning during my workouts. The only downside (not for this product) is that using the heart rate monitor drains my TomTom watch pretty quickly to where I'm charging it every day. The perk of being able to monitor my heart rate is more important to me than losing battery life in my watch.
4152428
Book is a great introduction into the PIG language and will get you writing Big Data analytics in no time.
174681
One of the things that is revolutionizing our lives right now is the big data phenomenon.  Most people are totally unaware of how it is affecting their lives every day but it is.  This did not contain what I was hoping but then again I do a lot of data analysis and was looking for information on advanced tools and techniques for handling big data.  On the other hand, it really is an excellent introduction to big data, how it is being used, and how it is changing our lives.

If you are already familiar with big data and the basics of how it affects us then you will probably want to look elsewhere, but if you want an introduction to the field then this is a good place to start.
1380290
One of the difficult jobs in big data is to get data of good quantity and *quality*. As a founder of an online dating site, the author is able to access gigantic data which has been collected over ten years. The data indeed shows who we are, and there are many surprises ;)
649394
As I've gotten concerned about storing my data in 3rd party clouds, I've been looking for alternatives that allow me to keep my data local.

I've got a transporter that we've migrated data over from a dropbox for teams account. This has saved us $700 a year and gives us peace of mind that the data is on drive we control. The transfer couldn't have been easier - drag and drop - and the performance has been bullet-proof. During the same period of time, we've had a number of dropbox outages.

The second device - a transporter sync - I'm using with a paid personal dropbox account. This gives be some redundancy and a really simple backup solution. Works like a charm!
1139747
Big Data is becoming ubiquitous these days, from soil research to fight global warming to appliances that can boost performance of enterprises at large. This scholarly researched book takes in depth study of Big Data appliances comparing and contrasting major offerings such as SAP HANA, IBM DB2 Blu accelerator and Oracle Exadata to name a few with tons of real-world case studies. At a time when the corporations are looking for uncluttered the data, advances in technology has enabled ultra-blazing speeds providing customers with data in the moment. This results in corporations making more timely and informed decisions. After reading this book, you will have a much better understanding of in-memory computing and the appliances for resolving performance challenges, reducing the complexity of IT landscapes comprising online transitional processing (OLTP) and data warehouse application as well as OLTP/OLAP blended systems. This book is must have for anyone from lay person to CEO of a corporation who wants to explore multiple big data tools with in-memory technology capabilities.
1211500
Ralph Kimball has rounded out his complete recipe for building fast, cost effective, robust and durable enterprise dimensional data warehouses with this immensely valuable addition to all IT & Data Warehouse professionals' bookshelves.

Without a  doubt ETL has been the biggest stumbling block to deploying and maintaining well architected data warehouses that stand the test of time.  Ralph draws on his years of experience and engagement with thousands of projects and crystallizes the `Best Practices' into an effective application architecture for all ETL systems regardless of what tools projects use for implementation.

In this thorough examination of the Extract, Transform and Load (ETL) process, Ralph identifies 38 critical functions that all ETL systems need to implement for success in the long haul.  He thoughtfully lays out simple and practical approaches for how each of these functions can be implemented by projects with any size of budget.

For many, the paradoxical nature of ETL (seeming trivial yet replete with endlessly complex details that constantly change) has been the proverbial straw that has broken the bank for many DW projects.  Continual customer pressure to grow, improve performance, and quickly deal with changing business conditions have left developers and architects grasping for more powerful and flexible approaches to ETL that meet project timelines, yet evolve and improve with age.  Armed with this enlightening roadmap, many DW professionals will be far better equipped to design and build systems that meet the challenges today and tomorrow.
3512538
This is a good primer on "big data" and gives good ideas on how to go about setting it up and mining the data.
3269960
Given the hyped data science and big data framework buzzwords, the topic this book covers is definitely relevant and important to big data practitioners. The author appears to have a long and solid experience in the industry which gave him much practical knowledge on the subject. Having quickly skimmed through the book, my first impression is the book has a broad coverage of Apache Hive, ranging from the basic setup to security, data manipulation and the detailed explanation on the grammar, complemented with relatively straightforward examples.

My current feeling is, as a thin book of 200 pages, it did quite a good job.
4361391
Beats both the Adata and PNY 32GB USB 3.0 flash drives on tested read and write times, and large file transfers.  Does get a bit warm on large file transfers, but if it is going faster it makes sense that more energy is being used.  Just how physics works.
4394640
The title cleverly positions the book in a market looking for thought leadership on big data. (use of data point: 150+ reader comments is a measure of success) Without actually defining "big data," authors illustrate the concepts and attributes of data with several examples. A smart move, considering the fact industry experts and gurus are hard pressed to converge on common definitions.

Author's narrative style of interspersing examples and references through the book reminded me of Steven Levitt's bestseller, Freakonomics. Many examples predate the "big data" hype cycle. In some cases, they are the same examples referenced by authors of business intelligence and data warehousing books.

What this book is: It is an excellent source of ideation on data, analytics, data warehousing and big data. Scores of references and mini "case studies" are used to illustrate the points, making for an interesting read.

The book is not: The book does not pretend to be, and is not, a practitioner's guide or a how-to reference. It is also not an "introduction" to big data.
2249124
It starts right from the very beginning where most Big Data books start from. The 3/5 Vs of Big Data, which is helpful for the beginner but might give a very mechanical feel to who already have been around the buzz a bit because it does not offer something different that you might not find elsewhere. Terminology, definitions, acronyms are explained in a very insipid and monotonous way. It could have been more interesting by not just stating stuff but explaining or giving analogies in a better way.

Every subtopic being point-wise does help in maintaining a flow and remembering stuff easily. The structure and the flow of the entire book is very logical and intuitive.
After introducing the concepts this book takes on the enterprise implementation of big-data problems/analytics. It focuses on getting the user acquainted with tools like Storm, Spark, Amazon kinesis and other skills required to quickly design, implement and deploy real-time solutions to big-data problems.

Over all, this book focus on implementation and not in-depth conceptual paradigms of Big Data. It is not recommended for explorers or deep-divers because it won't give you much understanding but simply helps you in knowing some tools in order to start implementing the solutions. A bit of background is needed in implementation and visualizing the problems before you take up this book. Else one can be lost in the whys and hows of it.

However, for a bit experienced developers this is handy to get going with the tools and sample implementation of solutions supported with codebase.
4733621
This is a fantastically written, easy to understand and comprehensive guide to security in Hadoop. The book is divided into 4 parts: 1. introduction and background of Hadoop, 2. authentication and authorization in Hadoop, 3. monitoring and logging, and 4. data protection by encryption. All these parts are very comprehensive in their own scope, which makes it easy for anyone with varying interests and knowledge in this domain to read this book. Just as the name suggests "Practical Hadoop Security", there is a pragmatic approach to this book, as it offers multiple screenshots, pieces of code and process descriptions. Bhushan Lakhe has done an incredible job in putting his Hadoop security insights, both at a high level and in-depth descriptions. This is a must read book, if you are in the Big Data industry or curious about the technologies safeguarding our data in Hadoop.
4185288
Aiden & Michel focus primarily on the pre-database 'science', then the story and algorithm development surrounding the Google Book project database.  Google Books is the largest dataset library of `words' on the planet. What might this dataset reveal? The author's had a role in Google Books and the Ngram algorithm toolset. The authors reflect entertaining observations of big data's potential. Uncharted includes an introductory history to the early 20th century science of word and language. Their history includes interesting but significant obscurities like Zipf's Law and the Haney index among others.

Big data is described as a new kind of science. This notion is not easily understood to be different until you look very closely at it. First, classical science observes nature, asks questions and designs experiments to reveal repetition. Second, the scientific method begins with the hypothesis to establish theory then proof .... unfortunaely societal trends defy predictive methods. The notion of 'word', or expression or concept and the application over time doesn't conform to scientific, quantitative, normative notions. So, the 'science' of big data behavior, independent of fully understanding underlying causalities and correlations, will be hotly debated. The Google Books library can tease notional theory from the current and exponentially growing data that humankind digitizes 5 zetabytes of data/yr at this moment.

The authors use their experience from Ngram development to provide simple 2D graphical images. The basic picture is of the frequency of a notions occurrence per billion words, or sets of words, over time.

So, as an example ... and it's just about Halloween, I can test for myself what I learned in "Uncharted" ... I can ask, "Is the world talking more about `vampires' or `zombies'"?  I just did an Ngram and can empirically state that vampires have been and continue to be the most discussed of the two societal threats. Further, vampires have increased to about a 4:1 lead over zombies in the database as of 2008. I can also see that "zombies" first appeared in the 1940's; `vampires' seem to have been around for ages or at least since before 1800. "Vampire" occurrences are rising exponentially; zombies arithmetically. We will have to wait to see if "World War Z" will affect the published world's threat assessment of zombies.

The authors expand the notion to real world considerations. Several specific `trend sets' were very interesting to this reviewer and they've consumed some post-read thinking time.  There is very much something here in the Uncharted big data world. It can be seen that after more than 200 years that we seem to have converged on the "chicken" vs "egg" and that's pretty big in itself.

The authors are interesting young gents who inject astute, off-the-wall commentary and deliver some very clever notions but ... the book reads as if novice writers are behind the word processor.  If the author's purpose is the novice style, they succeeded.  Some might find the lilting tech style off putting. The writing style aside, it's rare that I can read a book out of curiosity that delivers practical, ongoing self-entertainment skills. The fertilizer-for-the-brain effect of the appendix charts is worth the price of the book. You will need to decide for yourself to appreciate the unusual writing style.
4821509
This is a great book for understanding data.  So often folks tout "Big Data" and the wonders of it, but they are unable to really quantify the value of the data.  I personally work with large sets of data and work in reporting.  I thought this book was a good way to bridge the gap between folks that actually work with the data and those who are key holders in the value of the data.

I found this book to be really good to help discuss this in a way, this book I think has a great audience with those who are vested in big data, managing big data or selling the benefits of big data.
3992659
Understanding what is Big Data , what it is not, how it is important and where it matters are what you will gain from reading Too Big To Ignore.

The case studies alone will stimulate your mind in how your business will benefit when you apply the strategies of using big data laid out by Phil Simon.

When you want a simple, jargon free understanding of big data, you will appreciate the way Phil has delivered this important content.  Not knowing the power and uses of Big Data will be crippling very soon, do you really want to chance it?