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More sleep for Ops teams with Loggly and PagerDuty

By Pranay Kamat 04 Apr 2017

It is impossible for your Ops team to react quickly to every page and every possible infrastructure crisis. Every now and then, they must make trade-offs between different alerts and tickets. But when the clock is ticking and your customers are looking for a rapid response, your Ops team must have not only the best tools but also tools that allow you to scale as a business.

At Loggly, we’re excited to launch a new feature that does just that: automatic resolution of PagerDuty incidents.

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Focus the team on critical alerts

What can your Ops guy do when he has several pagers buzzing at the same time?  Prioritize and automate! By enabling our new feature, you can configure Loggly to automatically resolve PagerDuty incidents when the alert condition clears. This allows you to focus on longer-running, critical alerts instead of facing a deluge of short-lived actions.

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Loggly customers typically have numerous alerts with a variety of natural life spans.

Give everyone more rest with better alert management

Your Ops team is working hard to keep your systems up and running, often waking up in the middle of the night answering pages due to temporary system issues. Our new feature can add minutes to their uninterrupted sleep since they know Loggly has their back and will automatically close an incident if needed without requiring manual intervention from them.

If you’re already a Loggly customer sending alerts to PagerDuty, it’s time to help out your Ops team with automatic resolution. If you’re just getting started with Loggly, the PagerDuty integration, along with integrations to Slack, HipChat, VictorOps, and other Webhook-compatible notification systems, is included in the Loggly Pro and Enterprise plans.

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Pranay Kamat

Pranay Kamat Pranay Kamat is Senior Product Manager at Loggly. His previous experiences include designing user interfaces, APIs, and data migration tools for Oracle and Accela. He has an MBA from The University of Texas at Austin and Master's degree in Computer Science from Cornell University.