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.
Volker Pacher explains why Shutl chose Neo4j when faced with the need of building a new API meant to support business growth, the challenges met during implementation and solutions applied.
John O’Hara discusses banking business and technology integration, covering: low-latency, high-frequency trading, in-memory caches, multi-terabyte time-series databases, complex contracts in NoSQL stores and advanced systems integration.
Nicolas Spiegelberg discusses Facebook Messages built on top of HBase, the systems involved and the scaling challenges for handling 500TB of new data per month.
Tom Coupland discusses some of the various technologies investigated, and in many cases deployed at Nokia including Gradle, Spring, MongoDB and Clojure.
Randy Shoup details some of the pieces forming Google’s technology stack, BigTable, Megastore, Dremel, virtualization, etc. and the design principles of their their cloud-based applications.
CONTENT IN THIS BOX PROVIDED BY OUR SPONSOR
- 10 Things Developers Should Know about Couchbase
- When one is better than two: Collapsing data management layers for scalability and simplicity
- Couchbase NoSQL @ Tunewiki : A billion documents and counting
- The Essential Couchbase APIs Cheat Sheet
- Why MySQL 5.6 is no real threat to NoSQL
- How to Move from MySQL to Couchbase Server 2.0: Part 1
- Making Sense of NoSQL
- Couchbase in Action – Real world app demo
- Making the Shift from Relational to NoSQL