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GraphX Preview: Graph Analysis on Spark by Reynold Xin & Joseph Gonzalez, at Flurry in SF,.

Distributed Machine Learning using MLbase ( slides) by Evan Sparks & Ameet Talwalkar, at Twitter in SF,.Stores, Monoids & Dependency Injection - Abstractions for Spark ( slides) by Ryan Weald, at Sharethrough in SF,.Simple deployment w/ SIMR & Advanced Shark Analytics w/ TGFs ( slides) by Ali Ghodsi, at Huawei in Santa Clara,.SparkR and GraphX (slides: SparkR, GraphX) by Shivaram Venkataraman & Dan Crankshaw, at SkyDeck in Berkeley,.Adding Native SQL Support to Spark with Catalyst ( slides) by Michael Armbrust, at Tagged in SF,.Spark 1.0 and Beyond ( slides) by Patrick Wendell, at Cisco in San Jose,.In addition to the videos listed below, you can also view all slides from Bay Area meetups here. YouTube playlist of the Training Day (i.e.YouTube playlist of Track B (Spark Deployment, Scheduling & Perf, Related projects).YouTube playlist of Track A (Spark Applications).

Videos from Spark Summit 2013, San Francisco, Dec 2-3 2013.

Full agenda with links to all videos and slides.

Videos from Spark Summit 2014, San Francisco, June 30 - July 2 2013.

Screencast 4: A Spark Standalone Job in Scala.

Screencast 3: Transformations and Caching.

Screencast 2: Spark Documentation Overview.

Besides browsing through playlists, you can also find direct links to videos below. There are separate playlists for videos of different topics. See the Apache Spark YouTube Channel for videos from Spark events. In addition, this page lists other resources for learning Spark. The documentation linked to above covers getting started with Spark, as well the built-in components MLlib, Setup instructions, programming guides, and other documentation are available for each stable version of Spark below: