Big Data is one of the buzz-phrases that has become popular in recent years, especially in tech and business circles. Even though I am not a big fan of jargon and overhyped terminology, there are several real industry developments that justify the use of this particular term. With the ever increasing number of connected devices and sensors, the amount of data available for analysis has skyrocketed. This data has severely challenged the computing paradigms that were relevant just a decade ago, and forced a development of a whole new set of tools and technologies.

Big Data is a catch-all term for a wide variety of concepts and paradigms, and its protean nature allows for many different interpretations. So with that in mind, and due to to its vague title, I was not sure what to expect out of this short book. It turns out that this was really a (short) book length essay on various IT/ Big Data topics that the author finds important. The bulk of it talks about Hadoop, its history, and the origin of need for such a tool in enterprise. It’s a *very* bird’s-eye perspective, and you will not find any technological specifics here. The author also rails a lot against the “silos” model of division of labor/services in enterprise setting and strongly promotes the view that the optimal setup for most enterprises is centered around small teams of IT generalists who can understand the whole system on all of its levels. The author doesn’t provide any strong evidence to support any of his claims, and the book for the most part feels like a polemic. Nonetheless, it still manages to offer several grains of wisdom that can give one a better sense of the “big picture” of big data.