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Dynatrace observability is now available for Red Hat OpenShift on the IBM® Power® architecture

Dynatrace

We’re delighted to share that IBM and Dynatrace have joined forces to bring the Dynatrace Operator, along with the comprehensive capabilities of the Dynatrace platform, to Red Hat OpenShift on the IBM Power architecture (ppc64le). Dynatrace webhook server validates Dynakube definitions for correctness.

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The history of Grail: Why you need a data lakehouse

Dynatrace

Grail architectural basics. The aforementioned principles have, of course, a major impact on the overall architecture. A data lakehouse addresses these limitations and introduces an entirely new architectural design. It’s based on cloud-native architecture and built for the cloud. But what does that mean?

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7 Best Performance Testing Tools to Look Out for in 2021

DZone

The system could work efficiently with a specific number of concurrent users; however, it may get dysfunctional with extra loads during peak traffic. For example, the gaming app has to present definite actions to bring the right experience. Confirming scalability, dependability, stability, and speed of the app is crucial.

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What is a data lakehouse? Combining data lakes and warehouses for the best of both worlds

Dynatrace

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. This data lands in its original, raw form without requiring schema definition.

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Tips And Tricks For Evaluating UX/UI Designers

Smashing Magazine

Tips And Tricks For Evaluating UX/UI Designers. Tips And Tricks For Evaluating UX/UI Designers. When a company’s digital representation lacks a dedicated UX/UI design team, it can be hard to produce something that stands out from the crowd. That happens when your app/website/system is efficient. Nataliya Sambir.

Design 113
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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

We have deployed Auto Remediation in production for handling memory configuration errors and unclassified errors of Spark jobs and observed its efficiency and effectiveness (e.g., For efficient error handling, Netflix developed an error classification service, called Pensive, which leverages a rule-based classifier for error classification.

Tuning 210
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The state of site reliability engineering: SRE challenges and best practices in 2023

Dynatrace

Generative AI can also help improve root cause analysis by allowing users to ask specific questions regarding architecture and digital environments. The panelists speculated that AI will likely improve quality of life for SRE teams through its ability to efficiently execute tasks.