What to read after Big Data?

 In this book, the author tries to bring some clarity and much needed top-level understanding of Big Data — what it is, what makes it big, why is it important, and how do we all benefit from it. In the beginning chapters, the book covers the so-called ‘v’s of Big Data — volume, variety, velocity and veracity, as well as a few “minor” ‘v’s. It’s the size of these ‘v’s that gives Big Data its name. Historically, some of the technically most difficult issues with Big Data had to do with its storage and retrieval. Many of the best-known tech companies became the household names thanks to the expertise they developed for dealing with data at scale. In process they developed several advanced technologies and frameworks, many of which became open-sourced (such as Hadoop and Spark), and are now accessible to anyone. The issues and challenges of analyzing data at a large scale are indeed very difficult and need a very strong mathematics skill. I know that very well from firsthand experience many of my favorite techniques and methods that work very well with datasets that can fit in Memory are oftentimes virtually impossible to effectively implement with terabyte-sized data. In the last, the book covers the application of Big data in medicine and business. Untapped potential to use Big data, analytics, and machine learning in these fields. Big data relies on its effectiveness on the full digital transformation of organizations and processes, and, again speaking from experience, medicine and business have still a long way to go in this regard. Big data technologies will continue to have an outsized impact on our lives, and their influence is only going to grow. Anyone who would be interested in learning more about what Big data is all about would be well advised to read this book. It is informative, very well written, and relatively short and highly recommended.