Remove Entertainment Remove Infrastructure Remove Metrics
article thumbnail

Part 1: A Survey of Analytics Engineering Work at Netflix

The Netflix TechBlog

At Netflix, we seek to entertain the world by ensuring our members find the shows and movies that will thrill them. DataJunction: Unifying Experimentation and Analytics Yian Shang , AnhLe At Netflix, like in many organizations, creating and using metrics is often more complex than it should be. Enter DataJunction (DJ).

Analytics 212
article thumbnail

End-to-end observability provides deep insights into user behavior for British Columbia Lottery Corporation

Dynatrace

People now depend on digital experiences for access to goods, services, and entertainment. As such, the corporation’s mission is to deliver exceptional—and healthy—gambling entertainment experiences. Consequently, organizations need a way to capture and understand user behavior so they can make their services more reliable.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Netflix at AWS re:Invent 2019

The Netflix TechBlog

Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. Wednesday?—?December This session looks at what it takes to accept, produce, encode, and stream your favorite content.

AWS 38
article thumbnail

Telltale: Netflix Application Monitoring Simplified

The Netflix TechBlog

A metric crossed a threshold. Metrics are a key part of understanding application health. But sometimes you can have too many metrics, too many graphs, and too many dashboards. Telltale uses a variety of signals from multiple sources to assemble a constantly evolving model of the application’s health: Atlas time series metrics.

article thumbnail

Experimentation is a major focus of Data Science across Netflix

The Netflix TechBlog

Growth Advertising At Netflix, we want to entertain the world ! A Type-M error occurs when, given that we observe a statistically-significant result, the size of the estimated metric movement is magnified (or exaggerated) relative to the truth. Are there metrics that can yield a signal faster? What’s the tradeoff of using those?

article thumbnail

Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.

Systems 235
article thumbnail

Beyond REST

The Netflix TechBlog

Rapid Development with GraphQL Microservices by Dane Avilla The entertainment industry has struggled with COVID-19 restrictions impacting productions around the globe. Legitimate concerns about security (how does this integrate with our IAM infrastructure to enforce row-level access controls within the database?)

Database 205