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.
Pushpraj Shukla discusses how Microsoft Bing predicts the future based on aggregate human behavior using one of the largest scale data sets, and recent progress in large scale deep learnt models.
Martin Kleppmann explores using event streams and Kafka for keeping data in sync across heterogeneous systems, and compares this approach to distributed transactions.
Peter Bakas presents in detail how Netflix has used Kafka, Samza, Docker, and Linux to implement a multi-tenant pipeline processing 700B events/day in the Amazon AWS cloud.
David Talby demos using Python libraries to build a ML model for fraud detection, scaling it up to billions of events using Spark, and what it took to make the system perform and ready for production.