Architecting for Access: Simplifying Analytics on Big Data Infrastructure

January 22, 2019

Providing a full end-to-end solution requires not only evaluating a dozen or so technologies for each tier, but also looking at their many permutations. And at each juncture, we must remember we’re not just building technology for technology’s sake we’re hoping to provide analytics (often in near real time) that drive insight, action, and better decision making. Making decisions based on “data, not opinions” is the end game, and the technologies we choose must always be focused on that. It’s a dizzying task. Only by looking at the past, present, and future of the technologies can we have any hope of providing a realistic view of the challenges and possible solutions. This article is meant to be both an exploration of how the analytics ecosystem has evolved into what it is, as well as a glimpse into the future of both analytics and the systems behind it. By starting from both ends, we hope to arrive in the middle the present with a pragmatic list of requirements for an analytics stack that will deal with anything you can throw at it today and tomorrow. Before we can look at the requirements for a frontend analytics tool, however, it’s paramount that we look at the reality of the backend.

Spotlight

Cyient

Cyient (Estd: 1991, NSE: CYIENT) provides engineering, manufacturing, geospatial, network and operations management services to global industry leaders. We leverage the power of digital technology and advanced analytics capabilities, along with our domain knowledge and technical expertise, to help our clients solve complex business problems. As a Design-Build-Maintain partner that takes solution ownership across the value chain, we empower our clients to focus on their core, innovate, and stay ahead of the curve.

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