Trying to solve the data riddle purely through the lens of architecture is missing a vital point: The unifying factor across all data is a dependency on time. The ability to capture and factor in time is the key to unlocking real cost efficiencies.

Whether it’s streaming sensor data, financial market data, chat logs, emails, SMS or the P&L, each piece of data exists and changes in real time, earlier today or further in the past. Unless they are linked together in a way that firms can analyze, there is no way of providing a meaningful overview of the business at any point in time.

This talk will demonstrate how using kdb+, a columnar relational time-series database, with a tightly integrated query language called q, can do aggregations and consolidations on billions of streaming, real-time and historical records for complex analytics.

Part of Time Series Database Lectures 2017 Seminar Series

Bio:
Fintan Quill heads North American Software Engineering for Kx Systems. An expert in developing database analytic systems, Fintan joined Kx in 2012 focusing on sales of Kx Systems’ kdb+ database. In this role Fintan has built and tested analytical database systems for diverse datasets in a number of industries, including smart meter data, sensor data for precision manufacturing edge analytics and pharmacological research data. Prior to that, he worked extensively with quantitative teams at a variety of Wall Street investment banks, hedge funds, and trading shops building extremely fast technologies for structured Big Data applications. After beginning his career with First Derivatives, a global financial technology consultancy based in Newry, Northern Ireland, Fintan moved to the U.S., where he worked at investment banks including Nomura and Barclays Capital. Fintan is a graduate of Trinity College in Dublin with a specialization in Computing and Microelectronic Engineering. Fintan is a frequent speaker on database topics, including at Seattle Data Day, Austin Data Day, Strata+Hadoop World, the Irish Network USA and various Big Data Meetups around North America.