Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.
Mathieu Bastian explores the mechanics of unit, integration, data and performance testing for large, complex data workflows, along with the tools for Hadoop, Pig and Spark.
Adam Miskiewicz goes beyond the React Native docs and talks about best practices for building responsive and production-ready React Native applications with Redux, Relay, and GraphQL.
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.
Robert Metzger provides an overview of the Apache Flink internals and its streaming-first philosophy, as well as the programming APIs.
Karthik Ramasamy presents the design and implementation of Heron, the new de facto stream data processing engine at Twitter. Ramasamy shares Twitter’s experience of running Heron in production.
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.
Gian Merlino discusses stream processors and a common use case - keeping databases up to date-, the challenges they present, with examples from Kafka, Storm, Samza, Druid, and others.
Ian Bull introduces Node4J and explores the performance characteristics and highlights the tools that help one develop, debug and deploy Node.JS applications running directly on the JVM.
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.
Ali Kheyrollahi uses clustering and network analysis algorithms to analyze the publicly available Wiki data on rock music to find mathematical relationship between artists, trends and subgenres.