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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process. The Netflix video processing pipeline went live with the launch of our streaming service in 2007. The Netflix video processing pipeline went live with the launch of our streaming service in 2007.

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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Now let’s look at how we designed the tracing infrastructure that powers Edgar. Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices.

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data.

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Extend infrastructure observability with JMX Extensions and additional full-stack metrics

Dynatrace

Infrastructure exists to support the backing services that are collectively perceived by users to be your web application. Issues that manifest themselves as performance degradation on a user’s device can often be traced back to underlying infrastructure issues. Dynatrace news. Monitor additional metrics.

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Monitoring of Kubernetes Infrastructure for day 2 operations

Dynatrace

One of the promises of container orchestration platforms is to make i t easier for the developers to accelerate the deployment of their app lication s without having to worry about scalability and infrastructure dependencies. It is important to understand the impact infrastructure can have on the platform and the application it runs.

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Observability throughout the software development lifecycle increases delivery performance

Dynatrace

Today, development teams suffer from a lack of automation for time-consuming tasks, the absence of standardization due to an overabundance of tool options, and insufficiently mature DevSecOps processes. This process begins when the developer merges a code change and ends when it is running in a production environment.

Software 231
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Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

The Netflix TechBlog

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can