by O'Reilly Media, Inc., Nicole Tache | Released July 2016 | ISBN: 9781491970348

Watch this ongoing compilation of Spark talks from leading developers and practicioners at Strata + Hadoop World conferences in 2016 (including San Jose, London, Beijing, New York, and Singapore). Throughout the year we'll update the stream of talks covering the latest trends in Spark for real-time analytics and machine learning, as well as tips on how to deploy Spark in production. Topics range from Spark 2.0, anomaly detection with Spark, training deep networks in Spark, and tips for securing and scaling Spark in production. Show and hide more Publisher resources View/Submit Errata

1. San Jose 2016: Spark 2.0
   • The state of Spark and where it is going in 2016 - Reynold Xin (Databricks) 00:39:02
2. San Jose 2016: Spark Streaming
   • Apache Spark and real-time analytics: From interactive queries to streaming - Michael Armbrust (Databricks) 00:39:24
3. San Jose 2016: Spark and Machine Learning
   • Fast big data analytics and machine learning using Alluxio and Spark in Baidu - Bin Fan (Alluxio), Haojun Wang (Baidu) 00:27:37
   • SparkNet: Training deep networks in Spark - Robert Nishihara (UC Berkeley) 00:44:51
4. San Jose 2016: Spark in Production
   • Scala and the JVM as a big data platform: Lessons from Apache Spark - Dean Wampler (Lightbend) 00:39:44
   • Not your father's database: How to use Apache Spark properly in your big data architecture - Vida Ha (Databricks) 00:38:20
5. London 2016: Spark Streaming
   • The future of streaming in Spark: Structured streaming - Tathagata Das (Databricks) 00:41:57
   • So you think you can stream: Use cases and design patterns for Spark Streaming - Vida Ha (Databricks) and Prakash Chockalingam (Databricks) 00:40:51
   • Anomaly detection in telecom with Spark - Ted Dunning (MapR Technologies) 00:44:47
6. London 2016: Spark in Production
   • Securing Apache Spark on production Hadoop clusters - Kostas Sakellis (Cloudera) 00:40:19
   • Beyond shuffling: Tips and tricks for scaling Spark jobs - Holden Karau (IBM) 00:41:25
   • Adding complex data to the Spark stack - Neeraja Rentachintala (MapR Technologies) 00:39:56
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