Lawrence Chernin describes best practices and validation methods used to deal with large unstructured data, including a suite of unit tests covering the implementations of algorithmic equations.
Ali Jalali presents how to develop a machine learning predictive analytics engine for big data analytics.
Hilary Parker discusses the history of the analysis development tools, the current state of the art, and the importance for data scientists and analysts to understand programming principles.
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.
Graeme Seaton discusses the drivers behind Big Data initiatives and how to approach them using the vast amounts of data available.
Kriti Sharma talks about how Barclays is solving some of the toughest big data challenges in financial services using scalable, open source technology.
Viral Bajaria explains a formula for reaching the B2B buyer early in the sales cycle by tying together billions of rows of customer data and overlaying predictive intelligence 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.