Remove Definition Remove Design Remove Efficiency Remove Processing
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Best practices for Fluent Bit 3.0

Dynatrace

Fluent Bit is a telemetry agent designed to receive data (logs, traces, and metrics), process or modify it, and export it to a destination. Fluent Bit was designed to help you adjust your data and add the proper context, which can be helpful in the observability backend. Ask yourself, how much data should Fluent Bit process?

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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.

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

Dynatrace

By leveraging the Dynatrace Operator and Dynatrace capabilities on Red Hat OpenShift on IBM Power, customers can accelerate their modernization to hybrid cloud and increase operational efficiencies with greater visibility across the full stack from hardware through application processes.

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The history of Grail: Why you need a data lakehouse

Dynatrace

A data lakehouse addresses these limitations and introduces an entirely new architectural design. As a result, we created Grail with three different building blocks, each serving a special duty: Ingest and process. Ingest and process with Grail. A schema is a fixed definition to organize data in certain fields.

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

Dynatrace

These small wins, such as implementing a blameless root cause analysis process, can take many forms and don’t necessarily involve numerical metrics. This is made possible through generative AI’s natural language processing capabilities. SRE is about designing, building, and operating reliable services at scale,” said Townshend.