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Ensuring the Successful Launch of Ads on Netflix

The Netflix TechBlog

Next, we launched a Mantis job that processed all requests in the stream and replayed them in a duplicate production environment created for replay traffic. The Mantis query language allowed us to set the percentage of replay traffic to process. We continued ramping up and eventually reached 100% replay.

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. This approach has a handful of benefits.

Traffic 339
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Building High-Quality Software

DZone

It’s much better to build your process around quality checks than retrofit these checks into the existent process. NIST did classic research to show that catching bugs at the beginning of the development process could be more than ten times cheaper than if a bug reaches production. Metrics abstract you away from all details.

Software 245
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Interpreting A/B test results: false positives and statistical significance

The Netflix TechBlog

In Part 2: What is an A/B Test we talked about testing the Top 10 lists on Netflix, and how the primary decision metric for this test was a measure of member satisfaction with Netflix. This process generally starts by fixing the acceptable false positive rate. To build intuition, let’s run through a thought exercise.

Testing 226
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DynaWine: Transform faster with automation and AI

Dynatrace

Fermentation process: Steve Amos, IT Experience Manager at Vitality spoke about how the health and life insurance market is now busier than ever. As a company that’s ethos is based on a points-based system for health, by doing exercise and being rewarded with vouchers such as cinema tickets, the pandemic made both impossible tasks to do.

Speed 165
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Enhanced root cause analysis using events

Dynatrace

Getting the information and processes in place to ensure alerts like this example can be organizationally difficult. While you’re waiting for the information to come back from the teams, Davis on-demand exploratory analysis can proactively find, gather, and automatically analyze any related metrics, helping get you closer to an answer.

DevOps 186
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Using SLOs to become the optimization athlete with Dynatrace

Dynatrace

You’ll learn how to create production SLOs, to continuously improve the performance of services, and I’ll guide you on how to become a champion of your sport by: Creating calculated metrics with the help of multidimensional analysis. Metric 2: number of requests in error. Let’s start by creating a dashboard to follow our metrics.

Metrics 170