While basic monitoring services and feedback loops provide critical insight for DevOps teams, an end-to-end monitoring tool gives them a more holistic view of the end-user experience.

Does DevOps require end-to-end monitoring tools?

Feedback loops are critical for DevOps success. Knowledge of how changes affect users — and how they’re received — helps a DevOps team shape its approach throughout the application lifecycle.

To work, feedback loops require data. This data comes primarily from end users’ suggestions and help desk tickets, but a comprehensive monitoring tool is just as important to understand a DevOps environment’s health.

It’s critical to monitor service availability to provide better support and turnaround times when issues arise. Basic monitoring normally includes a variety of alerts for events such as a server going offline or a database becoming unavailable. These alerts’ relevancy depends on an IT environment’s topology, and high availability and redundancy configuration, but it’s useful to know something is off when it should be on.

Skim the waters with end-to-end monitoring

Rather than a system that evaluates each component individually, DevOps teams can use an end-to-end monitoring tool. These tools watch over the UX and return data such as wait time between request and resolution.

One particularly useful feature is the ability to see what stage of user request suffered a significant delay: Rather than knowing it took 10 seconds for the user to see a response, an IT admin can see that IIS Server 27 took eight seconds to get a response from SQL Server 2. This detail enables operations staff to communicate clearly any end-user experience issues they see, and funnel those issues to the most appropriate team.

This article originally appeared on Feedback loops are critical for DevOps success. Knowledge of how changes affect users — and how they’re received — helps a DevOps team shape its approach throughout the application lifecycle. .com To read the full article, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

If you would like to learn more, click here.