In this solutions track talk, sponsored by MongoDB, Matt Asay discusses the differences and tradeoffs between some of the NoSQL and SQL databases and when Hadoop makes sense to be used with a NoSQL solution.
In this solutions track talk, sponsored by Solace Systems, Aaron Lee discusses the value and challenges of efficiently moving information along with techniques and tools that can increase the rate and efficiency of data flows within big data architectures.
Gunter Dueck wonders how are we preparing for the new society marked by cloud computing and big data in which jobs are automated and mediocre abilities are no longer accepted?
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.
Akmal B. Chaudhri introduces Apache™ Hadoop® 2.0 and Yet Another Resource Negotiator (YARN).
Eva Andreasson presents typical categories of problems that are commonly solved using Hadoop and also some concrete examples in each category.
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.
Matthew Moloney discusses using F# and .NET inside Excel, demonstrating doing big data, cloud computing, using GPGPU and compiling F# Excel UDFs.
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.
Indrajit Roy presents HP Labs’ attempts at scaling R to efficiently perform distributed machine learning and graph processing on industrial-scale data sets.
Rusty Sears introduces REEF along with examples of computational frameworks, including interactive sessions, iterative graph processing, bulk synchronous computations, Hive queries, and MapReduce.
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.