Operations | Monitoring | ITSM | DevOps | Cloud

Many modern applications rely on MongoDB and MongoDB Atlas to manage growing data volumes and to provide flexible schema and data structures. As organizations adopt these and other NoSQL databases, effective monitoring and optimization become critical, especially in distributed environments.

Adopting a cloud-based, distributed architecture may help your organization scale quickly, but it can also add complexity. Correlating telemetry, security signals, and alerts across services often proves difficult, resulting in slower issue remediation. Additionally, when something goes wrong, figuring out who to contact—for example, the on-call responder or the service owner— may become needlessly time-consuming.

To ensure that your organization’s cloud spend is efficient, you need detailed and granular visibility to understand what comprises your costs, what causes them to change, and how the cloud services and resources you use are enabling your business goals. Extending your visibility and more closely monitoring your cloud costs can position you to successfully adopt FinOps, which provides a framework that can help you maximize the value you get from your cloud spend.

Microsoft Teams is deeply embedded in many organizations’ workflows, acting as a hub to both communicate and collect information about issues and ongoing projects. However, as with most communication platforms, it can be challenging to context-switch between conversations, tickets, and monitoring data when troubleshooting collaboratively.

As the number of distinct sources generating logs across systems and applications grows, teams face the challenge of normalizing log data at scale. This challenge can manifest when you’re simply looking to leverage logs “off-the-shelf” for investigations, dashboards, or reports–especially when you don’t control the content and structure of certain logs (like those collected from third-party applications and platforms).

We are thrilled to announce that Datadog has been named a Leader in the first ever 2024 Gartner Magic Quadrant for Digital Experience Monitoring. Datadog was positioned the highest in its Ability to Execute. We believe this placement reflects our commitment to being an end-to-end observability platform that brings together all signals from across your tech stack into a unified ecosystem.

As teams adopt OpenTelemetry (OTel) to instrument their systems in a vendor-neutral way, they often face a challenge in effectively tracing activity throughout their entire stack, from frontend user interactions to backend services and databases. While OTel enables basic tracing, teams still need a way to access advanced capabilities like continuous profiling to adequately optimize performance and troubleshoot issues in their applications.

Flaky tests are bad—this is a fact implicitly understood by developers, platform and DevOps engineers, and SREs alike. When tests flake (i.e., generate conflicting results across test runs, without any changes to the code or test), they can arbitrarily fail builds, requiring developers to re-run the test or the full pipeline. This process can take hours—especially for large or monolithic repositories—and slow down the software delivery cycle.

Azure OpenAI Service is Microsoft’s fully managed platform for deploying generative AI services powered by OpenAI. Azure OpenAI Service provides access to models including GPT-4o, GPT-4o mini, GPT-4 Turbo with Vision, DALLE-3, and the Embeddings model series, alongside the enterprise security, governance, and infrastructure capabilities of Azure.

Logs are a rich source of information, providing you with the minute details you need to troubleshoot a specific issue or perform extensive historical analysis. But with billions of logs being generated from your infrastructure every day, it isn’t practical to sift through them all to derive actionable insights. Firewall, CDN, network activity, and load balancer logs are especially high volume, requiring storage solutions that can be expensive and difficult to scale.