Remove Benchmarking Remove Code Remove Infrastructure Remove Lambda
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A look behind the scenes of AWS Lambda and our new Lambda monitoring extension

Dynatrace

Since its introduction by AWS in 2014, AWS Lambda has revolutionized the compute space and boosted the entire serverless movement. Dynatrace has offered a Lambda code module for Node.js since 2017, and many customers have used it with great success while we collected requirements for the next iteration of our Lambda extension.

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Lerner?—?using RL agents for test case scheduling

The Netflix TechBlog

Netflix engineers run a series of tests and benchmarks to validate the device across multiple dimensions including compatibility of the device with the Netflix SDK, device performance, audio-video playback quality, license handling, encryption and security. Detect a regression in a test case. Administrative user-interface to manage agents.

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Common use-cases for AWS Lambda in the enterprise (and how to get observability for them)

Dynatrace

AWS Lambda is enormously popular amongst our customers and while it was once perceived as just a new toy for startups who wanted to be at the cutting edge of technology, we’ve seen that many enterprise customers are now adding Lambda functions to their stacks. A quick primer on Lambda functions. Dynatrace news. Auto scaling.

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Common use-cases for AWS Lambda in the enterprise (and how to get observability for them)

Dynatrace

AWS Lambda is enormously popular amongst our customers and while it was once perceived as just a new toy for startups who wanted to be at the cutting edge of technology, we’ve seen that many enterprise customers are now adding Lambda functions to their stacks. A quick primer on Lambda functions. Dynatrace news. Auto scaling.

Lambda 179
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10X faster execution with compiled expression trees

Particular Software

It gets even worse when you don't even know exactly what the code is supposed to do until runtime. Customers can also define their own behaviors to address any system-wide concern at an infrastructure level rather than in every single message handler. If all we cared about was performance, we'd just hard-code the entire pipeline.

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