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.
Matthias Broecheler discusses graph computing, introducing the Aurelius graph cluster enabling graph computing at scale by building on distributed systems like Cassandra, HBase, and Hadoop.
Matthew Dennis covers the most common mistakes made with Cassandra that he has noticed being made both in deployment and code.
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.
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.
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.