Operations | Monitoring | ITSM | DevOps | Cloud

2023 was the year of Artificial Intelligence (AI). 2024 will build on the incredible momentum of the likes of ChatGPT, Google Bard, Microsoft CoPilot, and others, delivering applications and services that apply AI to every industry imaginable. A recent analysis piece from Schroders makes the point well: “The mass adoption of generative Artificial Intelligence (AI) …has sparked interest akin to the Californian Gold Rush.”

Deploying an infrastructure in the cloud has become THE standard across the industry. According to O’Reilley Research, more than 90% of organizations are on the cloud or are using it in some capacity. As reported by Precedence Research, cloud deployment will surpass $1 trillion by the year 2028. But with the adoption of a new platform comes the need for tools and best practices to keep it up and running efficiently, which includes cloud-based monitoring.

As businesses expand and adapt to the digital era, the need for scalable cloud services becomes paramount. Scalability—the ability of a system to handle growing workloads—ensures that enterprises can thrive without hardware constraints. Industry leaders such as Azure and AWS have been at the forefront of this evolution, continuously enhancing their platforms to provide seamless scalability.

Datadog Workflow Automation speeds up incident response and remediation for DevOps, SRE, and security teams by enabling them to automatically run predefined task sequences whenever specific alerts or security signals are triggered. After the feature’s initial release in 2023, Datadog is now excited to announce a significant expansion of its Workflow Automation capabilities with Azure actions, allowing engineers to create automated workflows for their Azure resources for the first time.

This blog post discusses utilizing Cribl Search to pull and visualize data from the AWS API without ingesting data. This will allow you to collect, analyze, and visualize data from your AWS account in real time without ingesting the data first.