Operations | Monitoring | ITSM | DevOps | Cloud

Automating data center tasks to increase operational efficiency is a primary goal for most data center professionals, but it can be a struggle. Professionals who have used legacy data center management tools like first-generation Data Center Infrastructure Management (DCIM) software, Excel, and Visio can attest to the fact that they are time-consuming, difficult to maintain, and inaccurate.

Within the world of robotics and automation one of the most recurring needs is that of capturing and visualizing real-time data from hardware components such as sensors and actuators, which provide insights into how a system is behaving overall and helps diagnose any potential issues that might arise overtime.

Welcome to Part 1 of our multipart series on Distributed Tracing for Full Stack Developers. In this series, we’ll be learning the ins-and-outs of distributed tracing and how it can assist you in monitoring the increasingly complex requirements of full stack applications.

Troubleshooting production issues with virtual machines (VMs) can be complex and often requires correlating multiple data points and signals across infrastructure and application metrics, as well as raw logs. When your end users are experiencing latency, downtime, or errors, switching between different tools and UIs to perform a root cause analysis can slow your developers down.

Node.js projects in Honeybadger can now officially take advantage of source maps, just like Client-Side JavaScript projects can.

Use this guide to write a golang application emitting traces using the OpenTelemetry Protocol (OTLP) Specification. All you need is a basic understanding of developing applications in golang.

I began my career as an associate software development engineer in June of 2020, and during my short time in the industry, I’ve had the opportunity to build and troubleshoot continuous integration and continuous development (CI/CD) pipelines, work on many different technologies within several SolarWinds®, formerly SentryOne, products, and learn proper engineering practices.

As technology advances and applications for the Internet of Things (IoT) continue to expand, industrial and manufacturing companies are embedding more digital systems into their operations. From smart factories and intelligent shipping to automation and 3D printing, Industry 5.0 has arrived.

Honeycomb is known for its incredibly fast performance: you can sift through billions of rows, comparing high-cardinality data across thousands of fields, and get fast answers to your queries. And none of that is possible without our purpose-built distributed column store. This post is an introduction to what a distributed column store is, how it functions, and why a distributed column store is a fundamental requirement for achieving observability.