Monday, May 9, 2016
Morning Workshops
W1: Introduction to Hadoop
9:00 a.m. - 12:00 p.m.
Hadoop has forever changed the economics and the dynamics of large-scale computing, and its use among enterprises looking to augment their traditional data warehouses continues to grow. Join this workshop to explore the basics of Hadoop, including the Hadoop Distributed File System, MapReduce, and the budding ecosystem of Hadoop software projects. Learn best practices for installing and configuring Hadoop in your environment, managing its performance, and developing Big Data applications. PLEASE NOTE: This is a hands-on workshop. Attendees are responsible for bringing their own laptop.
James Casaletto, Principal Solutions Architect, Professional Services, MapR
W2: Introduction to Spark
9:00 a.m. - 12:00 p.m.
Built for large-scale data processing, Spark is on the rise as one of the most active open source projects in the marketplace today. By enabling organizations to perform in-memory analytics on big datasets, it offers substantial performance benefits, as well as versatility as a multi-use platform for data scientists. This workshop provides an overview of its key features, ecosystem, and architecture, as well as how to run Spark, connect data sources, and start building applications. PLEASE NOTE: This is a hands-on workshop. Attendees are responsible for bringing their own laptop.
Marcin Tustin, Consulting Data Engineer
Afternoon Workshops
W3: Introduction to Data Science With Hadoop
1:30 p.m. - 4:30 p.m.
Data science, the ability to sift through massive amounts of data to discover hidden patterns and predict future trends, may be the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This workshop dives into the fundamentals of data exploration, mining, and preparation using Hadoop, as well as applying the principles of statistical modeling and data visualization in real-world applications.
Joe Caserta, Founding President, Caserta
W4: Introduction to Machine Learning With Spark
1:30 p.m. - 4:30 p.m.
From recommender systems to disease diagnosis, machine learning is revolutionizing the process of complex decision-making by enabling the analysis of bigger, more complex datasets and the delivery of faster, more accurate results. Spark offers a number of advantages that make it ideal for large-scale machine learning, including a library of machine-learning algorithms for large data. This workshop examines the statistical and algorithmic principles for developing scalable, real-world machine learning pipelines with Spark. PLEASE NOTE: This is a hands-on workshop. Attendees are responsible for bringing their own laptop.
Nathan P Halko, Data Scientist, Spotright