Ian Robinson discusses graphs data structures, some of the queries that can extract data from them, and tools and techniques to work with graph data.
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.
Erik Hinton discusses the successes and failures of making a cultural shift in the newsroom at NYT to accept Haskell and some of the projects Haskell has been used for.
Chris Anderson provides code samples on how to build offline applications for mobile platforms based on the NoSQL document model, and how to contribute to the open source projects behind this movement.
Matthew Moloney discusses using F# and .NET inside Excel, demonstrating doing big data, cloud computing, using GPGPU and compiling F# Excel UDFs.
Cliff Click introduces a coding style & API for in-memory analytics that handles datasets from 1K to 1TB without changing a line of code and clusters with TB of RAM and hundreds of CPUs.
Mridula Jayaraman shares from her experience developing a next generation sequencing solution used to customize cancer treatment based on patient's genetic makeup.
Ken Kousen advises Java developers how to do similar tasks in Groovy: building and testing applications, accessing both relational and NoSQL databases, accessing web services, and more.
Burt Beckwith discusses performing transactions in Grails, covering services, customizing transaction attributes (isolation, propagation levels), two-phase commit, using JMS, and testing the code.
Ryan Vanderwerf explains how to create and deploy a Grails application on AWS VPC using various services such as RDS, S3, autoscaling, S3FS, EBS, etc.
Chad DePue presents the process of building Edis, a Redis clone written in Erlang, allowing pluggable backends and implementing the Paxos algorithm.
Josh Wills discusses using Hadoop technologies to build real-time data analysis models with a focus on strategies for data integration, large-scale machine learning, and experimentation.
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