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.
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.
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.
Chad DePue presents the process of building Edis, a Redis clone written in Erlang, allowing pluggable backends and implementing the Paxos algorithm.
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.
Garrett Eardley explores how Riot Games is leveraging Riak for their stats system, discussing why they chose Riak, the data model and indexes, and strategies for working with eventually consistent data.
Sebastian Kanthak overviews Spanner, covering details of how Spanner relies on GPS and atomic clocks to provide two of its most innovative features: Lock-free strong (current) reads and global snapshots that are consistent with external events.
Paul King presents working with databases in Groovy, covering datasets, GMongo, Neo4J, raw JDBC, Groovy-SQL, CRUD, Hibernate, caching, Spring Data technologies, etc.
Jim Webber explores analytic techniques for graph data, discussing innate properties of (social) graphs from fields like anthropology and sociology. By understanding the forces and tensions within the graph structure and applying some graph theory, we'll be able to predict how the graph will evolve over time.
Siva Raghupathy discusses DynamoDB Design Patterns & Best Practices for realizing DynamoDB benefits at the right cost.
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- Making the Shift from Relational to NoSQL