Paul Moreno shows how to federate AWS IAM permissions, roles, and users with a directory service such as LDAP or Active Directory with an Identity Provider.
Ransom Richardson presents the Talko service architecture, its implementation and operation in the cloud, why they are using Erlang for it and key things learned along the way.
Ole Lensmar takes a look at a number of common API virtualization use cases - design-first development, sandboxing and load testing - to see how and when virtual APIs can help and when they can’t.
Tim Rath explains how and why Amazon incorporated more powerful testing methodologies, ultimately leading them to the use of formal methods where TLA+ has become a cornerstone to our overall strategy.
Sangeeta Narayanan goes over how Netfix got to the current continuous delivery state, the lessons they learnt and the successes they enjoyed along the way.
The authors take a deep dive into the history of NoSQL at Amazon.com, from the world of relational databases to the Dynamo days to the world of managed services like DynamoDB.
Aish Fenton discusses Netflix' machine learning algorithms, including distributed Neural Networks on AWS GPUs, providing insight into offline experimentation and online AB testing.
Derek Collison discusses some of the technologies and approaches for building a self-healing infrastructure: Intelligent layer 7 SDN with semantic awareness, self healing techniques, etc.
Chris Swan takes a look at Docker: what it is, why it was chosen, how it became an established platform, and what it takes to package applications and application infrastructure for use with Docker.
Adrian Cockcroft discusses strategies, patterns and pathways to perform a gradual migration towards modern enterprise applications based on cloud, microservices and denormalized NoSQL databases.
Shawn Gandhi overviews real-time processing use cases, and how developers are using AWS Kinesis to shift from a traditional batch-oriented approach to a continual real-time data processing model.
Randy Shoup describes KIXEYE's analytics infrastructure from Kafka queues through Hadoop 2 to Hive and Redshift, built for flexibility, experimentation, iteration, testability, and reliability.