In this webinar, Noni Peri, our Production Team Lead at Logz.io, discussed how to monitor Kafka’s performance and reliability by collecting and analyzing its JMX metrics with Logz.io

DevOps teams monitor their logs, metrics, and traces to identify and troubleshoot production issues in their Azure environments. But as they add new microservices, the scale and variety of telemetry data quickly becomes overwhelming. Learn how the Logz.io Observability Platform simplifies monitoring by providing the open source tools engineers prefer on a single, integrated platform purpose built for monitoring distributed cloud workloads.

Logz.io is a Cloud Observability Platform that helps engineering teams quickly identify and resolve production issues using the best open source for metrics and log monitoring available: Elk and Grafana. In this webinar, one of the engineers who built the product, Roi Ravhon, showed how the Logz.io engineering team uses Logz.io to deliver more reliable, performant, and secure services to our customers.

Secure your distributed cloud workloads with simplified, DevOps-native threat detection and security analytics.

Getting notified when critical events are taking place in your environment is crucial for fast and effective monitoring and troubleshooting. Logz.io has developed an elaborate alerting mechanism that allows users to create customized log-based alerts and share them across the organization.

Quickly identify, investigate, and resolve production issues in distributed environments with Logz.io’s Grafana-based metrics solution, Infrastructure Monitoring. See how easy it is to speed up root cause analysis by correlating between metrics and logs in just a few clicks.

Sift through millions of log messages in just a few clicks with Logz.io’s Log Patterns. Log Patterns uses advanced AI-powered clustering techniques to automatically analyze millions of log messages and identify recurring patterns in real-time. See how it helps streamline investigation and troubleshooting in our quick overview.

The dynamic nature of Kubernetes environments can make it difficult to collect and ship logs for analysis. Containers are constantly being moved, destroyed, and scaled up or down – it can be hard to track this dynamism.