Remove Design Remove Efficiency Remove Latency Remove Metrics
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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

You will need to know which monitoring metrics for Redis to watch and a tool to monitor these critical server metrics to ensure its health. Redis returns a big list of database metrics when you run the info command on the Redis shell. You can pick a smart selection of relevant metrics from these.

Metrics 130
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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Such frameworks support software engineers in building highly scalable and efficient applications that process continuous data streams of massive volume. Stream processing systems, designed for continuous, low-latency processing, demand swift recovery mechanisms to tolerate and mitigate failures effectively.

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Implementing AWS well-architected pillars with automated workflows

Dynatrace

This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. The framework comprises six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.

AWS 271
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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

Bringing together metrics, logs, traces, problem analytics, and root-cause information in dashboards and notebooks, Dynatrace offers an end-to-end unified operational view of cloud applications. Beyond SLAs, the emergence of machine learning technical debt poses an additional challenge for model observability.

Cache 226
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Dynatrace automatically monitors OpenAI ChatGPT for companies that deliver reliable, cost-effective services powered by generative AI

Dynatrace

A typical design pattern is the use of a semantic search over a domain-specific knowledge base, like internal documentation, to provide the required context in the prompt. With these latency, reliability, and cost measurements in place, your operations team can now define their own OpenAI dashboards and SLOs.

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Interpreting A/B test results: false negatives and power

The Netflix TechBlog

False negatives are closely related to the statistical concept of power , which gives the probability of a true positive given the experimental design and a true effect of a specific size. the difference in metric values between Groups A and B?—?the The variability of the metric in the underlying population. Sample size.

Testing 211
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Investigation of a Workbench UI Latency Issue

The Netflix TechBlog

Using this approach, we observed latencies ranging from 1 to 10 seconds, averaging 7.4 Blame The Notebook Now that we have an objective metric for the slowness, let’s officially start our investigation. Examining the code, we see that this function call stack is triggered when an API endpoint /metrics/v1 is called from the UI.

Latency 207