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Presentation: Azure Cosmos DB: Low Latency and High Availability at Planet Scale

InfoQ

Mei-Chin Tsai, Vinod discuss the internal architecture of Azure Cosmos DB and how it achieves high availability, low latency, and scalability. By Mei-Chin Tsai, Vinod Sridharan

Latency 52
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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. Upon further profiling, we found that most of the latency came from the candidate generated step (i.e.,

Tuning 210
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QCon London: Lessons Learned From Building LinkedIn’s AI/ML Data Platform

InfoQ

He specifically delved into Venice DB, the NoSQL data store used for feature persistence. The presenter shared the lessons learned from evolving and operating the platform, including cluster management and library versioning. By Rafal Gancarz

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Optimizing data warehouse storage

The Netflix TechBlog

This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture. Some of the optimizations are prerequisites for a high-performance data warehouse. Both automatic (event-driven) as well as manual (ad-hoc) optimization.

Storage 203