In the last decade, we have seen the impact of exponential advances in technology on the way we work, shop, communicate, and think. At the heart of this change is our ability to collect and gain insights into data; and comments like "Data is the new oil" or "we have a Data Revolution" only amplifies the importance of data in our lives.
Tim Berners-Lee, inventor of the World Wide Web said, "Data is a precious thing and will last longer than the systems themselves." IBM recently stated that people create a staggering 2.5 quintillion bytes of data every day (that's roughly equivalent to over half a billion HD movie downloads). This information is generated from a huge variety of sources including social media posts, digital pictures, videos, retail transactions, and even the GPS tracking functions of mobile phones.
This data explosion has led to the term "Big Data" moving from an Industry buzz word to practically a household term very rapidly. Harnessing "Big Data" to extract insights is not an easy task; the potential rewards for finding these patterns are huge, but it will require technologists and data scientists to work together to solve these problems.
The book written by Sunila Gollapudi, Getting Started with Greenplum for Big Data Analytics, has been carefully crafted to address the needs of both the technologists and data scientists.
Sunila starts with providing excellent background to the Big Data problem and why new thinking and skills are required. Along with a dive deep into advanced analytic techniques, she brings out the difference in thinking between the "new" Big Data science and the traditional "Business Intelligence", this is especially useful to help understand and bridge the skill gap.
She moves on to discuss the computing side of the equation-handling scale, complexity of data sets, and rapid response times. The key here is to eliminate the "noise" in data early in the data science life cycle. Here, she talks about how to use one of the industry's leading product platforms like Greenplum to build Big Data solutions with an explanation on the need for a unified platform that can bring essential software components (commercial/open source) together backed by a hardware/appliance.
She then puts the two together to get the desired result—how to get meaning out of Big Data. In the process, she also brings out the capabilities of the R programming language, which is mainly used in the area of statistical computing, graphics, and advanced analytics.
Her easy-to-read practical style of writing with real examples shows her depth of understanding of this subject. The book would be very useful for both data scientists (who need to learn the computing side and technologies to understand) and also for those who aspire to learn data science.
Broadridge Financial Solutions (India) Private Limited