Design Patterns for Big Data Architecture

Hi All,

We usually don't have an event in December - but this year we have a bonus event. MapR will be sponsoring the beer and pizza on the night. Hope you can come along to enjoy a Christmas drink with us and hear an interesting talk!

Allen Day: Principal Data Scientist at MapR Technologies

How do you make the best choices to keep architectural design for these projects simple yet powerful? This presentation describes new innovations for key big data architecture design patterns, from the technical details to real world use cases. Wouldn’t you like to be able to stream real-time data or query directly to a cluster? To simplify deployment of machine learning models in production? To easily incorporate web protocols into designs based on distributed data storage? This talk gives practical guidelines to show you how to efficiently integrate Hadoop-based computing with widely needed components that include real-time approaches such as Storm, search and index technology Solr, machine learning with Apache Mahout or enterprise solutions, and more.

Allen has been working on scientific computing and BigData problems for 15 years, first with Beowulf clusters and Hadoop since 2008. Allen created one of the first video discovery platforms for mobile phones, and also built the largest public database of human gene expression data. As an open source developer, Allen has authored R and Perl libraries and most recently contributed to Storm. Allen has founded and co-founded multiple machine learning and biotech companies, and he holds a Ph.D. in Human Genetics from the School of Medicine at UCLA.