DevOps teams often face challenges in troubleshooting incidents due to the sheer volume of log data, which can lead to delays in issue resolution and a degraded user experience. Zebrium addresses this by using unsupervised machine learning to identify root causes of incidents in logs, boasting a 95% accuracy rate according to a Cisco study. Zebrium has recently integrated with Datadog, allowing users to purchase a subscription through the Datadog Marketplace and enabling data streaming and alerting between the two platforms. This integration enhances incident response workflows by allowing the Zebrium app to enrich Datadog dashboards with root cause information, expediting troubleshooting without the need for manual log searches. An example involving an e-commerce site running on Amazon EKS illustrates how the Zebrium widget helps pinpoint the root cause of issues like network traffic drops, thereby reducing mean time to resolution (MTTR) and improving operational efficiency. The Zebrium app and integration are now available on the Datadog Integrations page, offering a streamlined process to identify and address system failures more effectively.