At observIQ, we've seen growing interest in observing the health of Windows systems and applications using OpenTelemetry. Requests on the SQL Server receiver continue to garner the most interest, so let's start there. Below are steps to get up and running quickly with the contrib distribution of the OpenTelemetry collector. We'll be collecting and shipping SQL Server metrics to a popular backend, Google Cloud.

In today's rapidly evolving landscape of software applications, where complexity often thrives, the need for observability and tracing has never been more pronounced. The ability to comprehend the inner workings of distributed systems and track the journey of requests as they traverse through various components is paramount for maintaining optimal performance and troubleshooting issues. This is where OpenTelemetry, a prominent observability framework, steps in.

In this post, we will talk about OpenTelemetry exporters. OpenTelemetry exporters help in exporting the telemetry data collected by OpenTelemetry. OpenTelemetry frees you from any kind of vendor lock-in by letting you export the collected telemetry data to any backend of your choice. In modern distributed systems, efficiently collecting, transmitting, and analyzing telemetry data from diverse sources poses a significant challenge.

Kubernetes is one of the most important and influential technologies for building and operating software today because it’s so incredibly capable. It’s flexible, available, resilient, scalable, feature-rich and backed by a global community of innovators — that’s a pretty impressive list of intangibles to apply to any particular capability.

To successfully observe modern digital platforms, a new data collection approach was needed. And OpenTelemetry (OTel) was the answer - an industry-agreed open standard - not a single vendor's approach - on how observability (O11y) data should be collected from a platform. This separates out data collection from the vendors’ platform of data processing and visualisation, making the data collecting approach vendor agnostic.

In the last 12 months, there’s been significant progress in the OpenTelemetry project -- arriving in the form of contributions, stability, and adoption. Being such, it felt a good time to refresh this post, providing project newcomers a short guide to get up and running quickly. In this post, I'll step through.

OpenTelemetry (OTEL) is an observability platform designed to generate and collect telemetry data across various observability pillars, and its popularity has grown as organizations look to take advantage of it. It’s the most active Cloud Native Computing Foundation project after Kubernetes, and it’s progressing at an immense pace on many fronts. The core project is expanding beyond the “three pillars” into new signals, such as continuous profiling.

PromCon, the annual Prometheus community conference, is around the corner, and this year I’ll have exciting news to share from the Prometheus Java community: The highly anticipated 1.0.0 version of the Prometheus Java client library is here! At Grafana Labs, we’re big proponents of Prometheus. And as a maintainer of the Prometheus Java client library, I highly appreciate the support, as it helps us to drive innovation in the Prometheus community.

In OpenTelemetry metrics, there are two temporalities, Delta and Cumulative and the OpenTelemetry community has a good guide on the different trade-offs of each. However, the guide tackles the problem from the SDK end. It does not cover the complexity that arises from the collection pipeline. This post takes that into account and covers the architecture and considerations that are involved end-to-end for picking the temporality.

Apache Kafka, born at LinkedIn in 2010, has revolutionized real-time data streaming and has become a staple in many enterprise architectures. As it facilitates seamless processing of vast data volumes in distributed ecosystems, the importance of visibility into its operations has risen substantially. In this blog, we’re setting our sights on the step-by-step deployment of a containerized Kafka cluster, accompanied by a Python application to validate its functionality. The cherry on top?