Kumar Palaniapan and Scott Fleming present how NetApp deals with big data using Hadoop, HBase, Flume, and Solr, collecting and analyzing TBs of log data with Think Big Analytics.
Jake Luciani introduces Brisk, a Hadoop and Hive distribution using Cassandra for core services and storage, presenting the benefits of running Hadoop in a peer-to-peer masterless architecture.
Siddharth Anand presents how Netflix’s architecture evolved from a traditional 3-tier configuration to a cloud-based one, detailing the scalability and fault tolerant issues encountered.
Chris Richardson shows how he ported a relational database to three NoSQL data stores: Redis, Cassandra and MongoDB.
Mike Malone discusses principles of good and bad (software) architecture determining SimpleGeo’s architecture: deal with change, embrace failure, phased adoption, balanced security, and others.
Kannan Muthukkaruppan overviews HBase, explaining what Facebook Messages is and why they chose HBase to implement it, their contribution to HBase, and what they plan to use it for in the future.
Siddharth “Sid” Anand explains the technical details behind the move from Oracle used inside their data center to SimpleDB and S3 in the cloud, and from there to Cassandra.
Ryan King presents how Twitter uses NoSQL technologies - Gizzard, Cassandra, Hadoop, Redis - to deal with increasing data amounts forcing them to scale out beyond what the traditional SQL has to offer
Kevin Weil presents how Twitter does data analysis using Scribe for logging, base analysis with Pig/Hadoop, and specialized data analysis with HBase, Cassandra, and FlockDB.
Marius Eriksen considers that leaky abstractions lead to scalability issues, while those providing narrow access to explicit resources - map-reduce, shared-nothing web apps, big table - scale better.
Eben Hewitt introduces the Apache Cassandra project to those interested in getting a quick clear picture of what Cassandra is, what are its main features, what is the the data model used and the API.