Metrics | Datadog

Unlock advanced query functionality with distribution metrics

Learn how Datadog distribution metrics summarize your data by providing globally accurate percentiles across ...

Timeseries indexing at scale

Learn how we implemented a new timeseries indexing strategy when the amount of data we ingested increased ...

How we built the Datadog heatmap to visualize distributions over time at arbitrary scale

Learn how we used DDSketch to enhance our heatmap visualizations, allowing us to represent and analyze high ...

Machine learning model monitoring: Best practices

Learn about key metrics and best practices for monitoring the functional performance of ML models to spot ...

A closer look at our navigation redesign

Learn more about our new redesign, including enhancements made to the navigability and readability of our ...

Expand your Sleuth monitoring reach with Datadog

Learn how Datadog gives you deeper insight into your Sleuth-tracked code deployments.

Introducing Boolean-filtered metric queries

Boolean operators enable you write more concise, expressive metric queries.

Datadog API client libraries now available for Java and Go

Use our API client libraries to programmatically collect, search, and update your monitoring data.

Auto-smooth noisy metrics to reveal trends

Datadog's new Auto Smoother function makes it simple to smooth out noisy metrics without losing sight of the ...

Simplify customer support with Datadog’s integrations for Zendesk

Learn how to track customer experience as your organization scales.

Metric graphs 101: Graphing anti-patterns

In this post, we explore three ways that metric graphs are commonly misused and then suggest better solutions ...

The power of tagged metrics

Tagged metrics let you add infrastructural dimensions to your metrics on the fly—without modifying the way ...

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