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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. We designed experimental scenarios inspired by chaos engineering.

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Enhancing Kubernetes cluster management key to platform engineering success

Dynatrace

Five of the most common include cluster instability, resource and cost management, security, observability, and stress on engineering teams. Engineering teams are overwhelmed with stuff to do.” The post Enhancing Kubernetes cluster management key to platform engineering success appeared first on Dynatrace news.

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Netflix Studio Engineering Overview

The Netflix TechBlog

Our mission in Studio Engineering is to build a unified, global, and digital studio that powers the effective production of amazing content. link] Why Does Studio Engineering Exist? Stay tuned as we expand on each stage of the content lifecycle over the coming months! What’s Next?

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What is chaos engineering?

Dynatrace

But with the complexity that comes with digital transformation and cloud-native architecture, teams need a way to make sure applications can withstand the “chaos” of production. Chaos engineering answers this need so organizations can deliver robust, resilient cloud-native applications that can stand up under any conditions.

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How Netflix Content Engineering makes a federated graph searchable (Part 2)

The Netflix TechBlog

By Alex Hutter , Falguni Jhaveri , and Senthil Sayeebaba In a previous post , we described the indexing architecture of Studio Search and how we scaled the architecture by building a config-driven self-service platform that allowed teams in Content Engineering to spin up search indices easily.

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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. We have also noted a great potential for further improvement by model tuning (see the section of Rollout in Production).

Tuning 210
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Ensure safe and secure releases at scale by providing Golden Paths

Dynatrace

DevOps practices have been established in the last decade to accomplish this goal and deal with the dynamics of modern, cloud-native software architectures. To bring these practices to life within an organization at scale, the discipline of platform engineering has gained popularity.