Spark-on-Yarn: The Road Ahead-(Marcelo Vanzin, Cloudera)
Hadoop and Spark-Perfect Together-(Arun C. Murthy, Hortonworks)
A Tale of a Data Driven Culture-(Gloria Lau, Timeful)
Exactly-Once Streaming from Kafka-(Cody Koeninger, Kixer)
Tagging and Processing Data in Real Time-(Hari Shreedharan and Siddhartha Jain, Cloudera and Salesforce)
Taming GC Pauses for Humongous Java Heaps in Spark Graph Computing-(Eric Kaczmarek and Liqi Yi, Intel)
Data Storage Tips for Optimal Spark Performance-(Vida Ha, Databricks)
Solving Low Latency Query Over Big Data with Spark SQL-(Julien Pierre, Microsoft)
Deep Dive into Project Tungsten: Bringing Spark Closer to Bare Metal-(Josh Rosen, Databricks)
Intro to Spark development
Building Large Scale Machine Learning Applications with Pipelines-(Evan Sparks and Shivaram Venkataraman, UC Berkeley AMPLAB)
Extending Word2Vec for Performance and Semi-Supervised Learning-(Michael Malak, Oracle)
Towards True Elasticity of Spark-(Michael Le and Min Li, IBM)
BlinkDB and G-OLA: Supporting Continuous Answers with Error Bars in SparkSQL-(Sameer Agarwal and Kai Zeng, Databricks and AMPLab UC Berkeley
Better Visibility into Spark Execution for Faster Application Development-(Shivnath Babubabu and Lance Co Ting Keh, Duke)
Field Notes from Expeditions in the Cloud-(Matt Wood, Amazon Web Services)
Lessons Learned with Spark at the US Patent & Trademark Office-(Christopher Bradford, Open Source Connections)
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
Communication Patterns with Apache Spark-(Reza Zadeh, Stanford)
Building a Data Warehouse for Business Analytics using Spark SQL-(Blagoy Kaloferov, Edmunds)