Netlify, a platform for frontend teams to build, deploy and scale modern web applications, announced a new integration with Datadog, Inc, the monitoring and security platform for cloud applications. Together, these solutions provide visibility into the operations of modern web applications, helping development teams to deliver a fast, error-free web experience and identify key visitor trends. Datadog customers can manage, search and analyze Netlify logs alongside data from other sources and services within their development tech stack.
Datadog provides customers unified, real-time visibility across their entire technology stack. Netlify provides a powerful, integrated platform and workflow for frontend development teams to build, run and scale modern websites and applications. Netlify’s Datadog integration uses Netlify’s new Log Drains functionality, allowing all w********** and serverless functions logs to be sent to Datadog for deeper review and correlation with data from other sources.
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Until now, Netlify customers had access to serverless functions log data inside the Netlify console. However, teams running production applications at scale were limited without traffic and functions logs being viewable within their primary monitoring tools. With this integration, teams using Datadog can now access and analyze those logs using their existing monitoring workflows and correlate them with data from across their web environment.
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With this new integration, Datadog and Netlify customers can:
- Track app performance over time or evaluate traffic patterns for security risks: Web teams on the Netlify platform can send a site’s traffic and serverless functions logs to Datadog for advanced querying, alerting and long-term archiving.
- Unify log data across platforms: Teams can create a highly customized dashboard that looks at website and web application log data in the full context of other key data that’s already tracked in Datadog.
- Learn faster from your logs: Process Netlify logs through Datadog’s field mapping pipeline to extract meaningful information and common attributes that can be reused as facets to better organize and search your logs.
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