Operations | Monitoring | ITSM | DevOps | Cloud

The early days of e-commerce on the internet resembled a digital Wild West, characterized by unencrypted form inputs and clear-text storage of sensitive information. Fast forward to today, and the landscape of online payments has transformed dramatically, bolstered by industry-driven guide rails like the Payment Card Industry Data Security Standard (PCI DSS). These standards ensure that consumer details are stored appropriately and handled with the utmost care and security.

There is so much good work that OpenTelemetry has done in the software industry, specifically around the domain of observability, in the last five years. Bringing users and vendors together to define the future of telemetry? Check! Unify logs, traces, and metrics under a completely vendor-neutral API? Check! Deprecate other standards by bringing their collaborators to the table to ensure their use cases are met? CHECK!

Jaeger and OpenTelemetry are essential technologies that greatly improve the observability of software applications. OpenTelemetry is a vendor-neutral platform that makes it easier to create and collect telemetry data, including logs, traces, and metrics. Its extensive backend integration adaptabilities allow it to fit into a wide range of infrastructures. However, Jaeger is an expert in distributed tracing within microservice environments.

The OpenTelemetry Transformation Language, or OTTL for short, offers a powerful way to manipulate telemetry data within the OpenTelemetry Collector. It can be leveraged in conjunction with OpenTelemetry processors (such as filter, routing, and transform), core components of the OpenTelemetry Collector. It caters to a range of tasks from simple alterations to complex changes.

If you want to see if OpenTelemetry helps you become a better Python developer — or if you just want to know how to add OpenTelemetry to your Python service — you’ve come to the right place. In this blog, we’ll show you how to instrument your Python application using OpenTelemetry and how to visualize your OpenTelemetry data using Application Observability in Grafana Cloud. We’ll walk you through the following steps.

As OpenTelemetry rises in popularity, more organizations are implementing, or planning to implement, the open source project to monitor their applications — and, meanwhile, more vendors are offering OpenTelemetry support. In fact, a quick Google search for “OpenTelemetry support” shows results ranging from legacy APM vendors to newer, cloud native solutions like Grafana Cloud.

Choosing an OpenTelemetry backend depends on several factors, such as your monitoring and observability requirements, current infrastructure, scalability needs, and budget constraints.

MongoDB’s flexibility and document-oriented nature have always stood out to me as its most compelling features, setting it apart from the strict schema constraints of traditional relational databases. This adaptability is a boon for application development, allowing for more dynamic data interactions that mirror real-world information complexities and freeing table schemas’ constraints.

In the dynamic world of observability and analytics, everyone’s looking for smarter, more efficient, and interoperable ways to handle their data. That’s where Cribl steps in, bringing you an exciting update to our product lineup. We’re thrilled to introduce the OTLP Metrics Function to Cribl Stream 4.5! This Function converts metrics into the OpenTelemetry Protocol (OTLP) format with ease!