Developing a successful Azure cloud operations (CloudOps) practice is the key to unlocking the full benefits of Microsoft’s Azure cloud platform. But Azure users often struggle to take full advantage of Azure’s benefits once they’ve made the move to the cloud. While it might be easy to build infrastructure inside of Azure and start spending money, ensuring your cloud infrastructure continues to support your organization’s business objectives as you scale is hard work.

Much has changed in the digital space, but one thing will remain important for organizations—safeguarding their entire digital environment. While data is the lifeblood of companies and businesses, protecting a digital environment goes beyond that. From confidential records to operating systems, applications to databases, the potential compromise of these components is a threat.

Rapid advancements in technology have welcomed the rise of cloud computing. This has changed how we access and manage IT resources, shifting the paradigm to more cloud-based management and monitoring. However, with so many moving parts, maintaining complete visibility and control can be a challenge. This is the hurdle that cloud-based monitoring tools are trying to overcome.

The world relies on the cloud which means that organizations are fundamentally reliant on third-party networks to access these critical cloud resources. While cloud adoption decisions are largely made by executive teams, the responsibility of performance and availability falls squarely on the shoulders of network operations teams.

Datadog Data Streams Monitoring (DSM) provides detailed visibility into your event-driven applications and streaming data pipelines, letting you easily track and improve performance. We’ve covered DSM for Kafka and RabbitMQ users previously on our blog. In this post, we’ll guide you through using DSM to monitor applications built with Amazon Simple Queue Service (SQS).

Recently, Elastic announced the AI Assistant for Observability is now generally available for all Elastic users. The AI Assistant enables a new tool for Elastic Observability providing large language model (LLM) connected chat and contextual insights to explain errors and suggest remediation.

Observability is the process of understanding complex systems by analyzing their outcomes and enhancing those outcomes by monitoring events within the system. Today, observability is essential for IT services to achieve a better user experience and optimize software performance. With cloud platforms dominating the IT services landscape, organizations are inclined to deploy their software and hardware systems in the cloud to reduce operational costs and enhance flexibility.

Managing application updates in production and ensuring the reliability of software releases in Kubernetes environments can be challenging. Small changes can sometimes lead to unforeseen issues in production. These unexpected problems, combined with the lack of scalability and the high costs associated with managing complex solutions, can be daunting.

In the digital age, efficient document management is more than a convenience—it’s a necessity. SharePoint, a product of Microsoft, stands out as a premier tool for organizing, storing, and retrieving documents. But the real game-changer within SharePoint is the feature known as “Document Sets.” Document Sets in SharePoint serve as a method to group related documents together, allowing them to be managed as a single unit.

AWS Elastic Beanstalk streamlines the deployment and management of applications in the cloud, offering developers an easy way to deploy services without worrying about the underlying infrastructure. However, while it simplifies many aspects of cloud deployment, it also presents challenges, including limited customization and potential vendor lock-in, which might not suit all project needs or scalability requirements.