Jim Porzak discusses creating an analyst ready data mart that is complete at different levels of abstraction and models customer decision points in order to be able to understand customers.
Kriti Sharma talks about how Barclays is solving some of the toughest big data challenges in financial services using scalable, open source technology.
Todd Brackley discusses accessing the “network of data” through a RESTful hypermedia API, exposing it to developers, testers, analysts and clients.
Irad Ben-Gal discusses Big Data analytics misconceptions, presenting a technology predicting consumer behavior patterns that can be translated into wins, revenue gains, and localized assortments.
Janet Wiener discusses using a data pipeline and graphic visualizations to extract and analyze the Chorus – the aggregated, anonymized voice of the people communicating on Facebook - in real time.
Sudhir Tonse discusses using stream processing at Uber: indexing and querying of geospatial data, aggregation and computing of streaming data, extracting patterns, TimeSeries analyses and predictions.
Helena Edelson addresses new architectures emerging for large scale streaming analytics based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK) or Apache Flink or GearPump.
Ali Kheyrollahi uses clustering and network analysis algorithms to analyze the publicly available Wiki data on rock music to find mathematical relationship between artists, trends and subgenres.
Joseph Paulchell discusses the journey from batch-oriented processes using databases to a real-time data streaming solution and the significant benefits achieved as well as the challenges encountered.
Phil Berman and Michael T Minella present a solution developed with Spring XD to stream real-time analytics from a moving car using open standards.
Jesus Rodriguez explores the characteristics of the IOT PaaS vs. predecessor PaaS architectures, focusing on device management, event driven integration, real-time analytics and offline communication.
Lyndon Maher, Paul McManus discuss data driven development, how to collect data, getting feedback, tools to use, and how to integrate a data-driven mentality into the team.