QCon is a practitioner-driven conference designed for technical team leads, architects, and project managers who influence software innovation in their teams.

Matt Asay, VP of Corporate Strategy at MongoDB

Matt Asay

Biography: Matt Asay

Matt Asay is the VP of Corporate Strategy at MongoDB. Before Matt was Vice President of Business Development at Nodeable (acquired by Appcelerator), a provider of real-time data stream processing for big data applications, and also at Strobe (acquired by Facebook). He was Chief Operating Officer at Canonical, ran the Americas for Alfresco and founded the Open Source Business Conference. Matt writes regular column for ReadWrite and The Register. He has a B.A. from Brigham Young University in English and Political Science, an M.A. from the University of Kent and a J.D. from Stanford University.

Twitter: @mjasay

Presentation: A Big Data Arsenal for the 21st Century

Track: Solutions Track Thursday 2 / Time: Thursday 10:20 - 11:10 / Location: Abbey Room

For some, Hadoop is synonymous with "Big Data," but Hadoop is just one component of a successful Big Data architecture. Depending on one's application, it may not even be the most important part.

NoSQL solutions like MongoDB also play a dominant role for storage and real-time data processing, helping companies keep pace with the scale of their data requirements. But NoSQL figures even more prominently in helping enterprises consume a wide variety of data sources at speeds not currently possible in Hadoop. NoSQL, then, offers a useful complement to Hadoop, as well as the transaction-based data of traditional RDBMSs.

Tackling Big Data is not a one-tool job, and so the orchestration of the appropriate NoSQL database with Hadoop and RDBMS is essential. In this session, we'll dig deep into the different types of NoSQL, identifying how they differ and the types of Big Data workloads for which they're best suited. We'll also explore the trade-offs one makes in choosing NoSQL databases like MongoDB or Neo4j over an RDBMS like MySQL, and when it makes sense to use both Hadoop and NoSQL and when it's more appropriate to use NoSQL on its own.