Designing and Architecting Distributed Data Platform using Microsoft’s BigData Technologies

Taking a tour of designing production scale Data Science Platform using Azure Cloud based Microsoft big-data and Machine-learning technologies. This session will present the industry’s best practices and patterns (with real-life examples) in designing and developing scalable and fault tolerant data platform. We will discuss multiple design choices (options) and the rationale behind choosing one over the others. I will also provide a high-level overview of current state (which has changed a lot since last code-camp) of various big-data technologies such as Hadoop, Spark, HBase, Hive running on top of Azure along with web based Machine Learning Studio running in Azure to design and architect Data Science applications. You don’t need to be familiar with it to attend this session.

Joy Chakraborty

Joy is a Distributed System Architect, 17+ yrs of Application Software development experience, 10+ yrs of .NET and C# development experience, 5+ yrs of work experience in ASP .NET web application scaling and performance improvement, 4+ yrs of WCF experience with a special interest in distributed/parallel computing, currently working on Cloud, Big Data and Machine Learning technologies for last 4+ years. Also, he is actively part of various Software architectural organization and active open source contributor on big-data projects.