52:36
Elements of Scale
Ben Stopford examines tools, mechanisms and tradeoffs that allow a data architecture to scale, from disk formats to fully blown architectures for real-time storage, streaming and batch processing.
44:41
Pulsar: Real-time Analytics at Scale
Sharad Murthy & Tony Ng present Pulsar, a real-time streaming system which can scale to millions of events per second with high availability and 4GL language support.
47:29
Machine Learning and IoT
Ajit Jaokar discusses data science and IoT: sensor data, real-time processing, cognitive computing, integration of IoT analytics with hardware, IoT’s impact on healthcare, automotive, wearables, etc.
48:46
How 30 Years of Ticket Transaction Data Helps you Discover New Shows!
Vaclav Petricek discusses how to train models, architect and build a scalable system powered by Storm, Hadoop, Spark, Spring Boot and Vowpal Wabbit that meets SLAs measured in tens of milliseconds.
45:01
Taking the Pain out of Real-time Mobile Back-end Development
Mandy Waite shows how to get started with Firebase before walking through a live demo of building a multi-user, collaborative mobile app that provides real-time updates to its users.
50:45
Java 8 in Anger
Trisha Gee uses Java 8 streams and lambdas to build an app consuming a real-time feed of high velocity data, using services to make sense of the data, and presenting it in a JavaFX dashboard.
36:35
Mini-talks: Deterministic Testing, Typesafe Config, Spreads v Probe, & Real-Time Event-Driven
Small sessions on: Deterministic testing in a non-deterministic world. Hash Spreads and Probe Functions. Typesafe Config on Steroids. Real-Time Distributed Event-Driven Computing at Credit Suisse.
48:50
Scaling Uber's Real-time Market Platform
Matt Ranney explains the Uber architecture overall, with a focus on the dispatch systems, the geospatial index, handling failure, and dealing with the distributed traveling salesman problem.
01:09:10
IoT Realized - The Connected Car
This session explores the power of Spring XD in the context of the Internet of Things (IoT).
35:16
Better Together - Using Spark and Redshift to Combine Your Data with Public Datasets
Eugene Mandel discusses challenges of conforming data sources and compares processing stacks: Hadoop+Redshift vs Spark, showing how the technology drives the way the problem is modeled.
51:34
Scalability Lessons from eBay, Google, and Real-time Games
Randy Shoup tells war stories from Google and eBay focusing on how to scale code, infrastructure, performance, and operations, along with hard-won lessons learned in scaling them.
01:03:55
Scalable Big Data Stream Processing with Storm and Groovy
Eugene Dvorkin provides an introduction to Storm framework, explains how to build real-time applications on top of Storm with Groovy, how to process data from Twitter in real-time, etc.
