Operations | Monitoring | ITSM | DevOps | Cloud

So, you've been tasked with automating one or more of your tedious, time-consuming IT processes… but what exactly does that mean? And perhaps more importantly, where on earth do you start? IT automation and orchestration can cover a broad spectrum of potential use-cases, ranging from the Service Desk to the NOC, to Infrastructure, and well beyond.

Redgate’s annual “State of the Database Landscape” survey has been published. Like every other year, it paints a really interesting picture. Personally, I love looking through this in order to better understand where people are experiencing pain in the management of their data. If you know where people are experiencing pain, as a technical person, you know where to focus your own skill development.

Modern software teams are under constant pressure to ship faster without breaking production. That’s why CI/CD best practices have become essential for high-performing DevOps organizations. Continuous integration and continuous delivery (CI/CD) help automate builds, testing, and deployments — but simply installing a pipeline tool isn’t enough. Without the right practices, pipelines become slow, flaky, and difficult to govern.

‍Flaky tests are automated tests that pass or fail inconsistently without changes to the code. In this guide, you’ll learn why flaky tests happen, how to detect them automatically in CI pipelines, and how modern platforms prevent them from slowing teams down. Your test went well three times yesterday. It didn't work this morning. You ran it again without changing anything, and now it works. Congratulations, you've just passed a flaky test, and now someone's day is going to be ruined.

Once upon a time, a monolith running on a handful of servers meant that incident management, even at 2:17 AM, was something a single generalist could handle. One person with enough context across the stack could reasonably diagnose whether the database was choking, a config had changed, or a server was running hot. They’d fix it and go back to sleep.

A deployment strategy is the method a team uses to move new code into a production environment. It determines how traffic shifts between versions, how much risk each release represents, and how quickly the team can roll back when something breaks. The choice isn’t academic: a mismatch between strategy and system can mean downtime, failed rollouts, or hours of manual recovery.

Data centers account for approximately 1–2% of global electricity consumption, a figure that continues to grow as demand for cloud computing, AI workloads, and digital services accelerates.

Snowflake is a cloud data platform designed for large-scale analytics, data warehousing, and data processing. It allows teams across an organization to run multiple data workloads on a single platform without managing infrastructure. Snowflake’s architecture is also unique. Compute and storage are completely independent and highly elastic. However, Snowflake’s per-second pricing and elastic compute model make costs highly sensitive to usage.

A multi-cluster Kubernetes architecture distributes application workloads across geographically separated clusters rather than a single environment. This strategy strictly isolates failure domains, ensures regional data compliance, and guarantees global high availability, but demands centralized Day-2 control to prevent exponential cloud costs and operational sprawl.