Architecting for Access
Simplifying Analytics on Big Data Infrastructure
By Rich Morrow
Released: August 2016
Fragmented, disparate backend data systems have become the norm in today’s enterprise, where you’ll find a mix of relational databases, Hadoop stores, and NoSQL engines, with access and analytics tools bolted on every which way. This mishmash of options presents a real challenge when it comes to choosing frontend analytics and visualization tools.
How did we get here? In this O’Reilly report, IT veteran Rich Morrow takes you through the rapid changes to both backend storage and frontend analytics over the past decade, and provides a pragmatic list of requirements for an analytics stack that will centralize access to all of these data systems. You’ll examine current analytics platforms, including Looker—a new breed of analytics and visualization tools built specifically to handle our fragmented data space.
Rich Morrow, a 20-year veteran of IT, is an expert on big data technologies such as Hadoop. He’s been teaching Hadoop and AWS for nearly three years, and uses these technologies in his day-to-day consulting practice. Rich is a prolific writer on cloud, big data, DevOps/agile, mobile, and IoT topics, having published many works for various companies, including GigaOM and Global Knowledge.