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.

Logz.io is one of Logz.io’s biggest customers. To handle the scale our customers demand, we must operate a high scale 24-7 environment with attention to performance and security. To accomplish this, we ingest large volumes of data into our service. As we continue to add new features and build out our new machine learning capabilities, we’ve incorporated new services and capabilities.

In my work as a technical evangelist at Cribl, I regularly talk to companies seeing annual data growth of 45%, which is unsustainable given current data practices. How do you cost effectively manage this flood of data while generating business value from critical data assets?

From Sumo Logic’s inception over a decade ago, we made a strategic bet to go all in with Amazon Web Service (AWS). Today, many of our customers rely on Sumo Logic to gain unified visibility into their growing number of AWS services, cut troubleshooting time and unlock comprehensive root cause analysis for complete issue resolution.

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.

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.

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.

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.

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.