Operations | Monitoring | ITSM | DevOps | Cloud

Organizations in every industry are becoming increasingly dependent upon data to drive more efficient business processes and a better user experience. As the data collection and preparation processes that support these initiatives grow more complex, the likelihood of failures, performance bottlenecks, and quality issues within data workflows also increases.

In today’s payments ecosystem, the ability to monitor and use payment data effectively represents a real and essential competitive advantage. Intelligent operations should be a strategic goal for the entire company, and when executed properly, will enable you to build a future-proof payment operations infrastructure.

OpenTelemetry (OTEL) is a big data platform that enables the collection and analysis of large-scale telemetry data. Many companies have adopted it for use in their products. In this post, we’ll discuss semantic conventions in OpenTelemetry and how they are used to make data processing easier. We’ll also discuss the different types of semantic conventions and their importance.

I care a lot about instrumentation and telemetry and OpenTelemetry, so I was thinking of joining the observability engineering team at my company… but it seems like they spend all their time managing Prometheus and Grafana. I guess I was expecting something very different?

Here at Grafana Labs, we’re always looking for ways to provide our customers with a choice of platforms where they can run Grafana Enterprise Metrics (GEM). As part of that mission, we’re pleased to announce that we’ve added Red Hat OpenShift 4.x support to GEM. GEM, as you may know, is a leading enterprise metrics solution.

Datadog has made a name for itself as a popular cloud-native application performance monitoring tool, measuring a system’s health and status based on the telemetry data it generates. This telemetry includes machine-generated data, such as logs, metrics and traces. Cloud based applications and infrastructure generate millions (even billions) of logs – and analyzing them can generate a wealth of insights for DevOps, security, product teams and more.