Neha Narkhede shares the experience at LinkedIn moving from ETL to real-time streams, the challenges of scaling Kafka to hundreds of billions of events/day, supporting thousands of engineers, etc.
Sameer Farooqui demos connecting to the live stream of Wikipedia edits, building a dashboard showing what’s happening with Wikipedia datasets and how people are using them in real time.
Charles Rivet introduces Papyrus RT, an industrial-grade modeling environment for the development of complex, software intensive, real-time, embedded, cyber-physical systems.
Ben Stopford discusses using stream processing tools for real-time business apps, handling infinite streams, leveraging high throughput, deploying dynamic, fault-tolerant, and streaming services.
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.
Jim Webber talks about several kinds of fraud common in financial services and how each decomposes into a straightforward graph use-case. He explores them using Neo4j and Cypher query language.
Joe Stein makes an introduction for developers about why and how to use Apache Kafka. Apache Kafka is a publish-subscribe messaging system rethought of as a distributed commit log.
Craig Berntson shows code samples for real world uses of SignalR: thermometers, alerts, non-web applications and others.
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.
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.
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.