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

The Netflix TechBlog

It provides a good read on the availability and latency ranges under different production conditions. These include options where replay traffic generation is orchestrated on the device, on the server, and via a dedicated service. Also, since this logic resides on the server side, we can iterate on any required changes faster.

Traffic 347
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No need to compromise visibility in public clouds with the new Azure services supported by Dynatrace

Dynatrace

Our customers have frequently requested support for this first new batch of services, which cover databases, big data, networks, and computing. The Azure MySQL dashboard serves as a comprehensive overview of your MySQL servers and database services. See the health of your big data resources at a glance.

Azure 227
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What is ITOps? Why IT operations is more crucial than ever in a multicloud world

Dynatrace

The roles and responsibilities of ITOps team members include the following: A system administrator configures servers, installs applications, monitors the health of the system, and fixes and upgrades hardware. This includes response time, accuracy, speed, throughput, uptime, CPU utilization, and latency. Performance. ITOps vs. AIOps.

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Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problem

The Netflix TechBlog

We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform. With large data, comes the opportunity to leverage the data for predictive and classification based analysis.

Big Data 188
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How observability analytics helps teams uncover answers

Dynatrace

While measuring app response time under different circumstances provides a latency value, for example, it doesn’t tell you why the app is slow, fast, or somewhere in between. These unknowns are often tied to the root cause of IT issues. Observability analytics can help teams solve for unknown unknowns.

Analytics 246
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Experiences with approximating queries in Microsoft’s production big-data clusters

The Morning Paper

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., Microsoft’s big data clusters have 10s of thousands of machines, and are used by thousands of users to run some pretty complex queries. Five queries improve substantially on both latency and total compute hours.

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Helios: hyperscale indexing for the cloud & edge – part 1

The Morning Paper

Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. What follows is a discussion of where big data systems might be heading, heavily inspired by the remarks in this paper, but with several of my own thoughts mixed in.

Cloud 104