Operations | Monitoring | ITSM | DevOps | Cloud

Cognitive Insights combines machine learning and crowdsourcing to automatically uncover critical issues in your log data together with actionable information to help you troubleshoot them faster.

Kibana is a powerful and flexible tool to search and visualize your logs in Elasticsearch – but only if you know how to use it! Zach Hamilton, a Sales Engineer at Logz.io, has enabled hundreds to be successful with Kibana and will provide his insights for best practices in this webinar. Understanding Kibana’s strengths and quirks can help you more efficiently explore your log data so you can quickly understand what’s happening in your environment.

You don’t have to choose between cost management and full observability. With Logz.io features Log Patterns, Drop Filters, and Archive & Restore add to cost efficiency. These features are designed to easily identify and filter out that data before indexing it, and then reingest that data if necessary later.

Observability teams today are facing increasing challenges in achieving visibility into the systems they monitor. Kubernetes adds significant complexity to achieving this goal. Open source provides powerful options and comes with serious advantages in cost, agility, and speed. Join Logz.io co-founder Asaf Yigal to learn how you can gain observability into Kubernetes projects with open source tools.

Containers get shipped by the thousand, but how do you keep track of the health and status of all those individual compute units? You can do it following the principles behind observability, you can see what’s happening with the infrastructure and the applications using open source tools. In this talk, we’ll describe how easy it is to implement the OSS tools required to get full observability and how that’ll improve your operations and your development cycles.

You don’t have to choose between cost management and full observability. With Logz.io features Log Patterns, Drop Filters, and Archive & Restore add to cost efficiency. These features are designed to easily identify and filter out that data before indexing it, and then reingest that data if necessary later.