Amazon Releases 'AWS X-Ray' Distributed Tracing Service in Preview by Daniel Bryant Posted on Dec 04, 2016
The AWS Well-Architected Framework Adds Operational Excellence by Abel Avram Posted on Nov 25, 2016
AWS Mobile Hub Improves Support for Serverless Apps, Authentication, and More by Sergio De Simone Posted on Nov 12, 2016
Amazon Kinesis Analytics is Like SaaS for Big Data Analysis by Elton Stoneman Posted on Sep 14, 2016
Kolton Andrus on Lessons Learnt from Failure Testing at Amazon and Netflix and New Venture Gremlin
Dec 02, 2016
Wesley Reisz talks to Kolton Andrus. Andrus is the founder of Gremlin Inc. He was a Chaos Engineer at Netflix, focused on the resilience of the Edge services. He designed and built FIT, Netflix’s failure injection service. Prior, he improved the performance and reliability of the Amazon Retail website.
Preslav Le on How Dropbox Moved off AWS and What They Have Been Able to Do Since
Nov 18, 2016
In March 2016 Dropbox announced that they had migrated away from Amazon Web Services (AWS). In this week's podcast Robert Bluman talks to Preslav Le about what they have been able to do since.
Virtual Panel: The Current State of Integration Platform as a Service (iPaaS) by Kent Weare Posted on Jul 04, 2016
Deploying Microservices to AWS at Gilt: Introducing ION-Roller by Natalia Bartol, Gary Coady, Adrian Trenaman Posted on May 08, 2015 11
Hologram - Finally, AWS Key Distribution that Makes Sense by Arian Adair Posted on Mar 31, 2015
DevOps in AWS LiveLessons Review and Q&A with Author Paul Duvall by Sergio De Simone Posted on Feb 12, 2015
Securing Servers in the Cloud: An Interview With Trend Micro by Richard Seroter Posted on Nov 13, 2013
Virtual Panel: Adjusting to Development in the Cloud by Richard Seroter Posted on Sep 20, 2013 1
InfoQ eMag: Scalability
This eMag examines topics such as how Twitter re-architected its code-base to improve stability and performance, the approaches Netflix uses to be hyper-resilient, and how Java is replacing C++ for low latency coding. We also look at some lower level tricks such as feedback controls for auto-scaling, and using memory and execution profiling to identify performance bottlenecks in Java.
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