An observability pipeline is a tool or process that centralizes data ingestion, transformation, correlation, and routing across a business. Production engineers across ITOps, Development, and Security teams use them to more efficiently and cost-effectively transform their telemetry data to drive critical decisions. Businesses of all sizes can enjoy several benefits and gain a significant competitive advantage by implementing an observability pipeline.

PU, memory use, latency, network bandwidth. These are just some of the monitoring metrics businesses analyze for security and performance. But successful data-driven organizations delve deeper than this. These companies probe millions of real-time metrics for unexpected insights and predict outcomes weeks, months, and years into the future. ELK helps them do this. It's a data analytics platform from open-source developer Elastic.

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.

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.

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.

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.

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.

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.

Monitoring distributed systems like MongoDB is very important to ensure optimal performance and constant health. But even the best monitoring tool will not be efficient without fully understanding the metrics it gathers and presents, what they represent, how to interpret them, and what they affect. That’s why it is crucial not only to collect the metrics but also to understand them.

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.