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44:53

A Research Agenda and Vision for Big Data at NASA

Posted by Chris Mattmann  on  Apr 28, 2014

Chris Mattmann covers four critical areas emerging in the context of NASA projects in radio astronomy; in snow hydrology and regional climate modeling; climate science, and in intelligence activities that together we must significantly advance to deal with the data deluge across NASA and government agencies.

45:22

Next Gen Hadoop

Posted by Akmal B. Chaudhri  on  Apr 22, 2014

Akmal B. Chaudhri introduces Apache™ Hadoop® 2.0 and Yet Another Resource Negotiator (YARN).

55:02

What Can Hadoop Do for You?

Posted by Eva Andreasson  on  Apr 22, 2014

Eva Andreasson presents typical categories of problems that are commonly solved using Hadoop and also some concrete examples in each category.

43:05

Design Patterns for Large-Scale Real-Time Learning

Posted by Sean Owen  on  Apr 15, 2014

Sean Owen provides examples of operational analytics projects in the field, presenting a reference architecture and algorithm design choices for a successful implementation based on his experience with customers and Oryx/Cloudera.

52:01

Excel Coding Errors Are Destroying World Economies and F# (with Tsunami) Is Here to Stop Them!

Posted by Matthew Moloney  on  Mar 05, 2014

Matthew Moloney discusses using F# and .NET inside Excel, demonstrating doing big data, cloud computing, using GPGPU and compiling F# Excel UDFs.

51:26

Scaling Pinterest

Posted by Yash Nelapati, Marty Weiner  on  Dec 30, 2013

Details on Pinterest's architeture, its systems -Pinball, Frontdoor-, and stack - MongoDB, Cassandra, Memcache, Redis, Flume, Kafka, EMR, Qubole, Redshift, Python, Java, Go, Nutcracker, Puppet, etc.

46:05

R for Big Data

Posted by Indrajit Roy  on  Dec 15, 2013

Indrajit Roy presents HP Labs’ attempts at scaling R to efficiently perform distributed machine learning and graph processing on industrial-scale data sets.

38:11

REEF: Retainable Evaluator Execution Framework

Posted by Rusty Sears  on  Dec 10, 2013

Rusty Sears introduces REEF along with examples of computational frameworks, including interactive sessions, iterative graph processing, bulk synchronous computations, Hive queries, and MapReduce.

33:33

Deploying Machine Learning and Data Science at Scale

Posted by Nick Kolegraff  on  Dec 01, 2013

Nick Kolegraff discusses common problems and architecture to support all the phases of data science and how to start a data science initiative, sharing lessons from Accenture, Best Buy, and Rackspace.

52:24

Big Data Platform as a Service at Netflix

Posted by Jeff Magnusson  on  Nov 18, 2013

Jeff Magnusson takes a deep dive into key services of Netflix’s “data platform as a service” architecture, including RESTful services that: provide comprehensive metadata management across data sources (Franklin); enable visualization and caching of results of Hadoop jobs (Sting); and visualize the execution plans produced by languages such as Pig and Hive (Lipstick).

39:26

Exercises in Style

Posted by Crista Lopes  on  Nov 13, 2013

Crista Lopes writes a program in multiple styles -monolithic/OOP/continuations/relational/Pub-Sub/Monads/AOP/Map-reduce- showing the value of using more than a style in large scale systems.

42:04

Stream Processing: Philosophy, Concepts, and Technologies

Posted by Dan Frank  on  Nov 10, 2013 1

Dan Frank discusses stream data processing and introduces NSQ – Bitly’s open source queuing system – and other new technologies used for communication between streaming programs.

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