OpenSearch was created by the community for the community to continue to keep an open-source alternative to ElasticSearch and Kibana. The project has been hard at work for the last 1.5 years building, launching and iterating on this important initiative. Some remarkable milestones have been achieved, including over 5,800 stars on GitHub with 19 different community-led projects.

There is a common painful workflow with many observability solutions. Each data type is separated into its own user interface, creating a disjointed workflow that increases cognitive load and slows down Mean Time to Diagnose (MTTD). At Coralogix, we aim to give our customers the maximum possible insights for the minimum possible effort. We’ve expanded our APM features (see documentation) to provide deep, contextual insights into applications – but we’ve done something different.

In this post, I want to scratch at the surface of a very interesting technology that Elastic’s Universal Profiler and Security solution both use called eBPF and explain why it is a critically important technology for modern observability. I’ll talk a little bit about how it works and how it can be used to create powerful monitoring solutions — and dream up ways eBPF could be used in the future for observability use cases.

Using Sumo Logic, you can analyze both metrics and logs simultaneously. Developed in 2010, this solution provides a powerful query language and scheduling support. Sumo Logic's production monitoring features provide visibility into production issues. Instead of manually writing alerts, the platform offers pre-configured alert templates (which Logit.io also offers), which makes setting up alerts easier and faster.

The market for enterprise observability solutions is growing in 2022, as organizations search for more effective ways to maintain security and oversight of increasingly complex and distributed IT systems. Traditional observability solutions like Splunk, Datadog and New Relic are still widely used by enterprises to analyze logs, metrics, and traces from their IT environments. But as enterprises generate increasing volumes of log data, two things tend to happen.

Logging as a Service, or LaaS, is a proven approach to managing and monitoring high-volume log data in modern dynamic environments. LaaS allows companies to manage log data regardless of whether it comes from applications, servers, or devices. With LaaS, companies can more easily aggregate and collate data, scale and manage storage requirements, set up notifications and alerts, and analyze data and trends. It also allows teams to customize dashboards, reports, and visualizations.

AWS recently introduced a new Lambda Telemetry API giving users the ability to collect logs, metrics, and traces for analysis in AWS services like Cloudwatch or a third-party observability platform like Coralogix. It allows for a simplified and holistic collection of observability data by providing Lambda extensions access to additional events and information related to the Lambda platform.

When teams collect data without full observability of what others on the team can see, it becomes clear that no one’s picture is truly accurate. In this picture, all of the people are wearing blindfolds and feeling around to see what is in front of them. One thinks this creature is a spear, another thinks it is a tree trunk, and another a rope. As long as they cannot observe what the others can, there is poor data fidelity.