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Part 1: A Survey of Analytics Engineering Work at Netflix

The Netflix TechBlog

This article is the first in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Subsequent posts will detail examples of exciting analytic engineering domain applications and aspects of the technical craft.

Analytics 212
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Cut costs and complexity: 5 strategies for reducing tool sprawl with Dynatrace

Dynatrace

Simplify data ingestion and up-level storage for better, faster querying : With Dynatrace, petabytes of data are always hot for real-time insights, at a cold cost. Business-focused, unified platform approach : A unified platform approach enables platform engineering and self-service portals, simplifying operations and reducing costs.

Strategy 165
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Build systems more reliably with Dynatrace: Chaos Engineering

Dynatrace

These releases often assumed ideal conditions such as zero latency, infinite bandwidth, and no network loss, as highlighted in Peter Deutsch’s eight fallacies of distributed systems. Chaos engineering is a practice that extends beyond traditional failure testing by identifying unpredictable issues.

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AI-powered DNS request tracking extends infrastructure observability for high quality network traffic

Dynatrace

With all the data collected and powered by our Davis AI-driven causation engine, Dynatrace automatically identifies slowdowns in your applications and services and points you to their root cause. Ensure high quality network traffic by tracking DNS requests out-of-the-box. Network services visibility (DNS, NTP, ActiveDirectory).

Traffic 264
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Network performance monitoring top of mind for CloudOps teams

Dynatrace

For cloud operations teams, network performance monitoring is central in ensuring application and infrastructure performance. If the network is sluggish, an application may also be slow, frustrating users. Worse, a malicious attacker may gain access to the network, compromising sensitive application data.

Network 246
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How Netflix Accurately Attributes eBPF Flow Logs

The Netflix TechBlog

By Cheng Xie , Bryan Shultz , and Christine Xu In a previous blog post , we described how Netflix uses eBPF to capture TCP flow logs at scale for enhanced network insights. Because the in-memory state can be quickly rebuilt when a FlowCollector node starts up, no persistent storage is required. With 30 c7i.2xlarge

AWS 188
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Optimizing data warehouse storage

The Netflix TechBlog

At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions) as the data lands into our warehouse. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 212