Operations | Monitoring | ITSM | DevOps | Cloud

Good people over at Splunk explain that the platform “removes the barriers between data and action, empowering observability, IT and security teams to ensure their organizations are secure, resilient and innovative.” Splunk is a unified security and observability platform that allows companies to go from visibility to action quickly and at scale.

As organizations are adopting more of the FinOps foundation practices and trying to optimize their cloud-computing costs, engineering plays an imperative role in that maturity. Traditional troubleshooting of applications nowadays relies heavily on static logs and legacy telemetry that developers added either when first writing their applications, or whenever they run a troubleshooting session where they lack telemetry and need to add more logs in an ad-hoc fashion.

Systems going down because of an unforeseen incident? Got problems with your app or website? Is your audience missing out on products and services because your load times are too slow? Then monitoring and observability (and telemetry) should be of interest to you! In this long article, we’re covering everything! I’ll start with the concepts and how they work.

Microservices testing is an essential part of any DevOps strategy. In a fast-paced environment like DevOps, you need real-time data on the deployment status and ensure your microservices integrations work correctly. The best way to achieve this is with frequent microservices testing.

As a Senior Product Manager at Mezmo, I understand the challenges businesses face in managing data complexity and the higher costs that come with it. The explosion of data in the digital age has made it difficult for IT operations teams to control this data and deliver it across teams to serve a range of use cases, from troubleshooting issues in development to responding quickly to security threats and beyond.

On their own, enterprise applications and systems are not always straightforward. Writ large, they are complex, integrated environments, full of multiple data formats and structures. You spend a great deal of effort and time to define and maintain diverse data models among these integrated components. A Canonical Data Model helps reduce that burden significantly — by promoting a standard and consistent data model between connecting components. This article describes a few things to get you started.