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The Real-Time Video Collection: 2016

Video Description

Learn about the very latest in real-time data processing tools, architectures, and applications with this video collection of select talks from the five Strata + Hadoop World conferences in 2016. You'll see world-class experts like Jay Kreps (Confluent) and Michael Armbrust (Databricks) discuss advances in tools such as Kafka and Spark, and leaders in the field explain how they design real-time platforms at scale, address common challenges, and implement streaming architectures across industries.

With The Real-time Video Collection: 2016, you get the full year of real-time talks, no matter when you purchase during 2016.

Here's how it works: After each conference wraps up, we'll update the video with an additional batch of talks. This year's conferences are being held in June, August, September and December. Content from Strata + Hadoop World San Jose, held in March 2016, is already available, and includes 11 talks from experts in real-time data processing.

Table of Contents

  1. San Jose 2016: Real-Time Tools
    1. Distributed stream processing with Apache Kafka - Jay Kreps (Confluent)
    2. Apache Spark and real-time analytics: From interactive queries to streaming - Michael Armbrust (Databricks)
    3. Fast data made easy with Apache Kafka and Apache Kudu (incubating) - Ted Malaska (Cloudera), Jeff Holoman (Cloudera)
    4. Apache Flink: Streaming done right - Kostas Tzoumas (data Artisans)
    5. Real-time Hadoop: What an ideal messaging system should bring to Hadoop - Ted Dunning (MapR Technologies)
    6. Designing a scalable real-time data platform using Akka, Spark Streaming, and Kafka - Alex Silva (Pluralsight)
    7. Pulsar: Real-time analytics at scale leveraging Kafka, Kylin, and Druid - Tony Ng (eBay, Inc.)
    8. Lessons learned building a scalable self-serve, real-time, multitenant monitoring service at Yahoo - Sumeet Singh (Yahoo), Mridul Jain (Yahoo)
  2. San Jose 2016: Real-Time Applications
    1. Taking Spark Streaming to the next level with DataFrames - Tathagata Das (Databricks)
    2. Real-time fraud detection using process mining with Spark Streaming - Hylke Hendriksen (ING)
    3. Uber, your Hadoop has arrived: Powering intelligence for Uber’s real-time marketplace - Vinoth Chandar (Uber)
  3. London 2016: Real-time Tools
    1. Triggers in Apache Beam (incubating): User-controlled balance of completeness, latency, and cost in streaming big data pipelines - Kenneth Knowles (Google)
    2. The evolution of massive-scale data processing - Tyler Akidau (Google)
  4. London 2016: Real-time Architectures
    1. Analytics for large-scale time series and event data - Ira Cohen (Anodot)
    2. Streaming analytics at 300 billion events per day with Kafka, Samza, and Druid - Xavier Léauté (Metamarkets)
  5. London 2016: Real-time Applications
    1. Real-time epilepsy monitoring with smart clothing: A case study in time series, open source technology, and connected devices - Eric Kramer (Dataiku)
    2. Kappa architecture in the telecom industry - Ignacio Manuel Mulas Viela (Ericsson) and Nicolas Seyvet (Ericsson AB)