Operations | Monitoring | ITSM | DevOps | Cloud

For Apple users, the iCloud Drive app is the default option for storing files in the cloud on your iPhone, iPad, or Mac computer. iCloud Drive integrates well with the Apple product suite as many apps built for macOS and iOS use iCloud by default for syncing, and it's the only option for syncing photos in the Photos apps among the Apple product suite. While many people may opt for the convenience and integration of iCloud Drive for their Apple products, others are not so keen.

Is your Azure spend management getting out of control? You’re not alone. Countless businesses struggle with the exact problem, often lacking clarity on the reasons behind the cost spike. Especially within larger organizations, where multiple teams deploy Azure resources, the Azure costs can quickly get out of control without the necessary tools to effectively track the spending and evaluate monthly Azure spend against their business needs.

Ensuring efficient deployment and management of containerized applications is critical to development teams across all possible industries in today’s cloud-native world. A standout service for many has been AWS Elastic Container Service (ECS), which has proven to be scalable and easy to use while integrated into one of the largest cloud ecosystems out there. However, one size does not fit all.

Tidal Accelerator has recently made its powerful Business Analytics feature generally available across all existing and new workspaces, as discussed in our previous article. This game-changing capability empowers organizations to make data-driven decisions throughout their migration and modernization journeys. In this article, we’ll dive deeper into four key areas where Tidal Accelerator’s advanced analytics provide invaluable insights.

As businesses increasingly move their workloads to the cloud, managing cloud costs has become a critical concern. At Tidal.Cloud, with the roll-out of new features such as Tidal Accelerator’s Business Analytics, we recently realized that our AWS costs were steadily increasing, and we needed to take action to optimize our cloud spending.

From its initial appearance in the dev-tools space, GenAI has had an outsized impact on how developers approach day-to-day tasks (just ask any developer about when they first started using GitHub’s copilot). While any risks are still being evaluated—like potential for introducing anti-patterns or inadvertently running afoul of compliance requirements, many engineering teams have successfully implemented GenAI with measurable gains in collaboration and productivity.

Have you ever wondered what goes behind those annoying 'Oops, something went wrong' messages on the web? You're not alone. A simple Google search for the most common HTTP errors throws up names we've all seen at least once: Not Found, Unauthorized, Forbidden, Internal Server Error, and Bad Request. It's like a secret code that both frustrates and fascinates.

This blog post explores the technical and strategic benefits of deploying open-source AI models on Ubuntu. We’ll highlight why it makes sense to use Ubuntu with open-source AI models, and outline the deployment process on Azure.

Ever since generative AI burst into public consciousness, business executives and IT leaders have been intrigued by the potential of generative AI to transform IT. As it turns out, generative AI has already begun revolutionizing ITOps and we are excited to be at the forefront of this game-changing technology through the ScienceLogic SL1 Hollywood release.

There’s a sentence that strikes fear into the heart of every frontend developer I've ever met: Users are reporting issues, and we don't know how to replicate them. What do you do when that happens? Do you cry? Do you mark the issue as wontfix and move on? Personally, I took the road less traveled: gave up frontend engineering and moved into product management (this is not actually accurate but it's a good joke and it feels truthy).