Pat Patterson and Ted Malaska talk about current and emerging data processing technologies, and the various ways of achieving "at least once" and "exactly once" timely data processing.
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.
Andrew Psaltis talks about Apache Beam, which aims to provide a unified stream processing model for defining and executing complex data processing, data ingestion and integration workflows.
Kriti Sharma talks about how Barclays is solving some of the toughest big data challenges in financial services using scalable, open source technology.
Tim Wagner defines server-less computing, examines the key trends and innovative ideas behind the technology, and looks at design patterns for big data, event processing, and mobile using AWS Lambda.
Adeel Ali presents insights from his database of 11,500 real world APIs.
Christos Erotocritou introduces Apache Ignite, discussing how it is used to solve some of the most demanding scalability and performance challenges. He covers typical use cases and examples.
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.
Shelby Switzer discusses success stories and failures of using the public data provided by governments, along with techniques for making such data usable.
Matthew Sackman discusses dependencies between transactions, how to capture these with Vector Clocks, how to treat Vector Clocks as a CRDT, and how GoshawkDB uses them for a distributed data store.