Operations | Monitoring | ITSM | DevOps | Cloud

The modern software development lifecycle comprises two key phases: continuous integration (CI) and continuous delivery or deployment (CD). In both stages, automation reduces manual labor, minimizing human errors. That enables DevOps teams to focus on mission-critical work instead of continually fixing mistakes. Automating CI/CD also provides a check and balance system for rolling back errors. Aside from ensuring optimal system performance, this also improves other areas, like security and compliance.

In a DevOps setting, incident management is all about quickly identifying, analyzing, and fixing issues that disrupt IT services. Unlike traditional IT Service Management (ITSM), which often works in isolated teams, DevOps encourages collaboration between development, operations, and business teams. This teamwork ensures that when problems like server outages or software bugs occur, they are handled swiftly and effectively. DevOps incident management is all about being agile and flexible.

This is a guest post from Mri Pandit. Information is the cornerstone of business operations, and data is now the most critical asset for any organization. But as reliance on data grows, so do the risks associated with breaches, fraud, and non-compliance. For most organizations, particularly in highly regulated sectors like finance, security and compliance are no longer optional – they’re essential for survival.

Many of my customers are using Flyway Enterprise to create migration scripts that will then be used to deploy database changes. They’ve been using Flyway Desktop, but some of them have expressed interest in using command line instead of the desktop.

Choosing the right container engine for your infrastructure stack is a critical architectural decision. While both Podman and Docker implement OCI (Open Container Initiative) standards, their fundamental approaches to container management and runtime architecture create distinct operational characteristics.

Learn how to keep your APIs running smoothly! From tracking performance to boosting reliability, this guide has everything developers need.

Learn the key differences between MongoDB and Elasticsearch, and understand when to use each for your database and search needs.

As businesses realized the potential of artificial intelligence (AI), the race began to incorporate machine learning operations (MLOps) into their commercial strategies. But the integration of machine learning (ML) into the real world proved challenging, and the vast gap between development and deployment was made clear. In fact, research from Gartner tells us 85% of AI and ML fail to reach production.

In today’s digital-first world, organizations are constantly pressured to deliver faster, more reliable, and secure network services. Yet, with a staggering 65% of enterprise network activities still performed manually, the journey toward network automation is more critical than ever.

We’ve all been there: working a feature that spans multiple Git repositories, doing your best to coordinate pull requests and save the reviewers from “review-hell” as they try to piece together how everything fits across the repositories… When a change impacts more than one microservice, the coordination can become a mess of disconnected PRs spanning many repos, each dependent on the other to avoid breaking something critical.