Operations | Monitoring | ITSM | DevOps | Cloud

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.

The promise of cloud computing has always been about flexibility and cost-effectiveness. Yet many organizations find themselves trapped in a cycle of unpredictable costs and underutilized/overutilized resources. The culprit? A lack of understanding about the power of scaling within platforms like Amazon’s Elastic Kubernetes Service (EKS).

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.

Cost optimization in cloud environments is no longer a luxury — it’s a necessity. As businesses increasingly migrate to AWS, they are met with the difficult task of ensuring robust performance and scalability, all while keeping an eagle eye on costs. How does one achieve this delicate balance? How can organizations ensure that their AWS architectures are both high-performing and cost-efficient?

Whenever it comes to a small-scale private cloud infrastructure project roll-out, organisations usually face a serious dilemma. The implementation process often seems complex due to a lack of knowledge, tricky migrations and an immediate need from management to run various extensions, such as Kubernetes, on top.

Using OpenTelemetry Collector, you can collect metrics from Azure monitor and export them to any backend of your choice. Azure Monitor is a powerful service within the Microsoft Azure ecosystem that provides extensive metrics and logging capabilities. Yet the siloed nature of data in such tools can obscure the bigger picture, hindering a holistic view of system health. In this tutorial, we cover: If you want to jump straight into implementation, start with this Prerequisites section.

Cloudwatch is a standard component for any AWS user, with tight integrations into every AWS service. While Cloudwatch initially seems like a cost-effective solution, its lack of functionality and flexibility can result in higher costs. Let’s explore Coralogix vs Cloudwatch.