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Ensuring the Successful Launch of Ads on Netflix

The Netflix TechBlog

We informed our engineering and business partners, including customer support, about the experiment and ramped up traffic incrementally while monitoring the success and error metrics through Lumen dashboards. We continued ramping up and eventually reached 100% replay. At this point we felt confident to run the replay traffic 24/7.

Traffic 342
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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

The second phase involves migrating the traffic over to the new systems in a manner that mitigates the risk of incidents while continually monitoring and confirming that we are meeting crucial metrics tracked at multiple levels. The batch job creates a high-level summary that captures some key comparison metrics.

Traffic 339
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Efficient SLO event integration powers successful AIOps

Dynatrace

However, it’s essential to exercise caution: Limit the quantity of SLOs while ensuring they are well-defined and aligned with business and functional objectives. Data Explorer “test your Metric Expression” for info result coming from the above metric. In other words, where the application code resides.

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Interpreting A/B test results: false negatives and power

The Netflix TechBlog

We then used simple thought exercises based on flipping coins to build intuition around false positives and related concepts such as statistical significance, p-values, and confidence intervals. the difference in metric values between Groups A and B?—?the The variability of the metric in the underlying population. Sample size.

Testing 202
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Uplevel your gamechanging skills at Perform 2022

Dynatrace

Attendees will have the same great Dynatrace experts teaching the sessions live via webcam (and we’d highly encourage you to turn on your webcam as well), the same virtual classroom to join and complete the hands-on exercises, and the same great content.

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Building High-Quality Software

DZone

They are rerun(in the best case) and thus defeating the whole purpose of this exercise while spending tons and tons of time/money/energy on this).nn> Metrics abstract you away from all details. Metrics abstract you away from all details. As soon as some process (like gathering metrics) becomes a goal (vs. A side note.

Software 245
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How ShiftLeft Uses PostgreSQL Extension TimescaleDB

High Scalability

We have two types of time series data: metrics and vulnerability events. Metrics represent application events, and a subset of those that involve security issues are vulnerability events. Vulnerability events can also have an event sample that contains detailed information about the request that exercised a security vulnerability.