Operations | Monitoring | ITSM | DevOps | Cloud

One of the major trends in software development in the last decade has been “shifting left” responsibilities that have traditionally been under operation’s domain to earlier in the software development life cycle (SDLC). It first came in the form of DevOps where a lot of the software engineering best practices were introduced to the deploy, operate, monitor phases. Such examples include continuous integration and continuous deployment (CI/CD) and Infrastructure as Code (IaC).

Tracing provides indispensable insights into the state and performance of distributed applications, but it can often be difficult to determine the root cause or ultimate business impact of issues indicated by traces. Translating visibility of individual microservices into broader performance insights often requires drawing complex correlations between spans. This can be a laborious process, which can complicate everything from troubleshooting and triage to tracking KPIs and managing costs.

Complex systems require many different monitors to assess the health of their infrastructure and applications, creating a wealth of alerts that can be hard to track. Due to a lack of effective triage processes, many organizations page engineers for every alert that comes in, making it difficult to separate false positives from issues that actually require immediate attention.

Database Administrators (DBAs) rely on time series data every day, even if they don’t think of time series data as a unique data type. They rely on metrics such as CPU usage, memory utilization, and query response times to monitor and optimize databases. These metrics inherently have a time component, making them time series data. However, traditional databases aren’t specifically designed to handle the unique characteristics and workloads associated with time series data.

The Sysdig 2024 Cloud‑Native Security and Usage Report highlights the evolving threat landscape, but more importantly, as the adoption of cloud-native technologies such as container and Kubernetes continue to increase, not all organizations are following best practices. This is ultimately handing attackers an advantage when it comes to exploiting containers for resource utilization in operations such as Kubernetes.

Reporting and analytics dashboards provide enhanced visibility into your data and the ability to view your most critical metrics via a single source of truth. By using dashboards, your team can easily highlight issues or areas of concern and promptly begin addressing them utilizing the real-time data that a dashboard provides. As well as this they can be utilized to drive data-driven decisions for your organization, enabling greater accuracy for decision-making to drive growth.

Cribl Stream offers different ways to optimize data, such as: In this blog, I will focus on the Aggregation use case using the Aggregations function and how you can practically use the Aggregations function to format the output in different ways.

Imagine checking a status page during a service disruption only to be greeted by a generic and impersonal display, devoid of any brand identity or relevant information. A status page without customization feels detached and fails to provide a good digital user experience. In addition, a status page that doesn't match your brand's look and feel can make the communication seem mundane.