Remove Event Remove Latency Remove Metrics Remove Systems
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Rapid Event Notification System at Netflix

The Netflix TechBlog

To this end, we developed a Rapid Event Notification System (RENO) to support use cases that require server initiated communication with devices in a scalable and extensible manner. In this blog post, we will give an overview of the Rapid Event Notification System at Netflix and share some of the learnings we gained along the way.

Systems 334
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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

The Machine Learning Platform (MLP) team at Netflix provides an entire ecosystem of tools around Metaflow , an open source machine learning infrastructure framework we started, to empower data scientists and machine learning practitioners to build and manage a variety of ML systems. ETL workflows), as well as downstream (e.g.

Systems 226
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DevOps automation: From event-driven automation to answer-driven automation [with causal AI]

Dynatrace

They need event-driven automation that not only responds to events and triggers but also analyzes and interprets the context to deliver precise and proactive actions. These initial automation endeavors paved the way for greater advancements, leading to the next evolution of event-driven automation.

DevOps 219
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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. This significantly increases event latency.

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What is observability? Not just logs, metrics and traces

Dynatrace

As dynamic systems architectures increase in complexity and scale, IT teams face mounting pressure to track and respond to conditions and issues across their multi-cloud environments. How do you make a system observable? Dynatrace news. Why is it important, and what can it actually help organizations achieve? What is observability?

Metrics 363
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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

Behind the scenes, a myriad of systems and services are involved in orchestrating the product experience. These backend systems are consistently being evolved and optimized to meet and exceed customer and product expectations. It provides a good read on the availability and latency ranges under different production conditions.

Traffic 339
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Migrating Netflix to GraphQL Safely

The Netflix TechBlog

So, we relied on higher-level metrics-based testing: AB Testing and Sticky Canaries. To determine customer impact, we could compare various metrics such as error rates, latencies, and time to render. The AB experiment results hinted that GraphQL’s correctness was not up to par with the legacy system.

Traffic 353