Operations | Monitoring | ITSM | DevOps | Cloud

To prevent visibility gaps in your cloud environment, you need to efficiently deploy observability solutions that integrate easily with key technologies in your stack and scale reliably with new applications and migrated workloads. But observability deployments can be complex, often requiring deep and specific knowledge that may not be available within your teams.

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).