Datadog Announces Integration with Microsoft Azure DevOps

Datadog Announces Integration with Microsoft Azure DevOps

Datadog, the monitoring and analytics platform for developers, IT operations teams and business users in the cloud age, announced a new integration with Microsoft Azure DevOps. Users can pull in events, derive metrics from their continuous integration pipelines, and correlate this information with real-time data from their entire stack in Datadog. In addition, teams can set Datadog monitors as gates in their Azure DevOps deployment pipelines, enabling them to detect and abandon bad releases automatically before users are impacted.

“We’re excited that users will no longer need to jump between tools to monitor how deployments might be impacting their applications”

Companies are increasingly adopting continuous integration and continuous deployment best-practices to improve the speed and agility of their developer operations. As part of this, many teams rely on services like Azure DevOps in order to release more frequent, smaller updates to their code. As updates proliferate, monitoring this activity in context with application performance data becomes critical. Without this visibility, determining a root cause and fixing bugs in dynamic environments can be very time consuming, resulting in extended outages.

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“We’re excited that users will no longer need to jump between tools to monitor how deployments might be impacting their applications,” said Steve Harrington, Product Manager at Datadog. “With the ability to stop Azure DevOps deployments automatically based on issues detected in Datadog, this integration provides a powerful tool for mitigating or preventing downtime altogether.”

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In addition to troubleshooting deployments, managers can also use this integration to understand the efficiency of their devops processes. Datadog generates metrics to help track Azure DevOps events, so users can see how often builds are completing or failing, the frequency of code commits, and the duration of work items. This data provides useful feedback for teams to track and improve their workflows as they implement Agile methodology.

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