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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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Unlock log analytics: Seamless insights without writing queries

Dynatrace

What about correlated trace data, host metrics, real-time vulnerability scanning results, or log messages captured just before an incident occurs? Dynatrace automatically puts logs into context Dynatrace Log Management and Analytics directly addresses these challenges. This context is vital to understanding issues.

Analytics 267
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Automate complex metric-related use cases with the Metrics API version 2

Dynatrace

Dynatrace collects a huge number of metrics for each OneAgent-monitored host in your environment. Depending on the types of technologies you’re running on individual hosts, the average number of metrics is about 500 per computational node. Running metric queries on a subset of entities for live monitoring and system overviews.

Metrics 246
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Why log monitoring and log analytics matter in a hyperscale world

Dynatrace

Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. What is log analytics? Log analytics is the process of evaluating and interpreting log data so teams can quickly detect and resolve issues.

Analytics 264
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Analytics at Netflix: Who we are and what we do

The Netflix TechBlog

Analytics at Netflix: Who We Are and What We Do An Introduction to Analytics and Visualization Engineering at Netflix by Molly Jackman & Meghana Reddy Explained: Season 1 (Photo Credit: Netflix) Across nearly every industry, there is recognition that data analytics is key to driving informed business decision-making.

Analytics 243
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Introducing Impressions at Netflix

The Netflix TechBlog

Analytical Insights Additionally, impression history offers insightful information for addressing a number of platform-related analytics queries. We accomplish this by gathering detailed column-level metrics that offer insights into the state and quality of each impression.

Tuning 166
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Data lakehouse innovations advance the three pillars of observability for more collaborative analytics

Dynatrace

The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. Grail combines the big-data storage of a data warehouse with the analytical flexibility of a data lake. With Grail, we have reinvented analytics for converged observability and security data,” Greifeneder says.

Analytics 246