Operations | Monitoring | ITSM | DevOps | Cloud

Development teams build modern applications using microservice architectures. Individual services are built and maintained by separate teams, and then these services are combined using container-based orchestrators to comprise a complete product offering. Microservices are a standard development method because they allow teams to iterate releases, providing ongoing new customer-facing features and bug fixes without needing to redeploy an entire platform or app.

For most organizations, GitHub is mission critical. Your GitHub repositories likely also contain some of your organization’s most sensitive data. GitHub provides tools to help you protect and govern this data, with tools such as audit logs, code scanning alerts, and secret scanning alerts. However, analyzing these logs and alerts through GitHub’s UI can be challenging. For example, looking for trends in your code scanning alerts over time through GitHub’s UI is just not possible.

Observability is the ability to see and understand the internal state of a system from its external outputs. Logs, Metrics, and Traces, collectively called observability data, are three external outputs widely considered to be three pillars of observability. Now more than ever, organizations of all sizes must employ the necessary processes and technologies to harness the power of their data and make it more actionable.

Data enrichment is the process of adding additional context or attributes to telemetry data at the source that increases its value during analysis. OpenTelemetry, a collaborative open source telemetry project with the largest organizations in the observability space, can be configured to enrich logs and metrics from dozens of sources. This blog will show you the basics of how to use BindPlane OP to easily deploy and configure OpenTelemetry to enrich data from a source.

Earlier this month I hosted the “Everything You’ve Heard About Observability is Wrong (Almost)” webinar– thanks to all of you who attended. I wanted to follow-up with the attendees as well as those who were not able to join. As promised, it wasn’t the same old Observability presentation that we have grown accustomed to you know, all marketing with little value.

Splunk and Amazon Web Services (AWS) are celebrating 10 years of strategic collaboration — an incredible milestone which demonstrates our commitment to teamwork, co-innovation and exceptional, data-driven outcomes for our joint customers.

So your team has access to a logging tool? Great! What’s the first thing you want to find? The latest config change gone wrong? Data from 30 days ago when a specific server was at high capacity? Or maybe you’d like to access logs for a certain IP on a certain day for specific HTTP and servers with counts and averages. Hopefully there was training to teach you the specific query languages and expert skills required to answer these questions.

Logs are a critical aspect of any production workload, as they give you insight into what is happening in your system and tell you which components may be having issues. The traditional method of looking at logs involves basic Linux commands like tail, less, or sometimes cat.

Large organizations often rely on multiple monitoring tools, security platforms, and auditing systems to meet the diverse needs of their observability, security, engineering, and compliance teams. Because these teams may use the same logs for many different use cases—including detecting potential threats or breaches, troubleshooting errors, and gauging the effectiveness of new features—it can be difficult to effectively standardize and route data.

Whether you’re rushing to troubleshoot an incident or proactively performing a security audit, the trial-and-error process of searching through millions of logs for key information can be time-consuming and cumbersome. To help you quickly surface important details from large swaths of log data, Datadog’s Log Explorer allows you to search and filter your logs, create visualizations, as well as group your logs by fields, patterns, or transactions.