Datadog Announces Machine-Learning Based Anomaly Detection For Cloud Applications
Datadog, the essential monitoring service for modern cloud environments, today announced the release of a new machine-learning based feature called Anomaly Detection. This will allow engineering teams to quickly identify abnormal behavior within rapidly changing cloud environments, based on historical patterns that are impossible to track manually.
"We are analyzing nearly a trillion data points every day coming from some of the largest companies in the world," said Homin Lee, Lead Data Scientist at Datadog. "Our algorithms are rooted in classic statistical models but have been heavily adapted and optimized by Datadog for monitoring cloud applications."
Anomaly Detection works by constantly analyzing historical application performance data, in order to evaluate whether the current state is to be expected by comparison. This is different from traditional monitoring solutions that pre-define what should be considered normal behavior of the application, without taking into consideration seasonality and trends. Application throughput, web requests, user logins and other top-level metrics all have pronounced peaks and valleys, and those fluctuations make it diffcult to manually set sensible thresholds for alerting or investigation.
"It can be challenging to manually configure alerts for metrics which change throughout the day, week, or year," said Igor Serebryany, Developer Happiness Engineer at Airbnb. "Anomaly detection helps us respond to issues more quickly, while avoiding needlessly paging our engineers."
Another existing function of Datadog's alerting engine is a feature called Outlier Detection, which triggers an alert when a server is behaving significantly different from its counterparts at a given moment. Combined with the algorithmic alerting of Anomaly Detection and Datadog's dashboarding technology, engineering teams gain deep insights into how their application is performing.
Datadog is the world's leading monitoring service for cloud-scale applications, bringing together data from servers, databases, tools, and services to present a unified view of your entire stack. These capabilities are provided on a SaaS (News - Alert)-based data analytics platform that enables Dev and Ops teams to work collaboratively to avoid downtime, resolve performance problems, and ensure that development and deployment cycles finish on time. Since launching in 2010, Datadog has been adopted by thousands of enterprises including Airbnb, Twilio (News - Alert), Zendesk, Atlassian, and Salesforce.
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