Use case scenarios

Obcerv solves some of the following pain points:

Log investigation Copied

I need to investigate and see if the same error is in my other logs.

Large amounts of log data collected from log monitoring can be stored in Obcerv. Together with log data, Obcerv also has the ability to store and analyse metrics. This means a shift from monitoring error messages and log files to investigating problems across your IT estates. You have more visibility on patterns such as if similar problems have happened elsewhere, how many times has a particular error occurred before, and whether errors happen at a particular time of day. This reveals helpful context in identifying entities, attributes, and keywords that trigger alerts.

For more information, see Logs.

Alert aggregation Copied

Help me reduce noise from all of my monitoring tools.

On any given day, monitoring tools can generate hundreds and hundreds of events. This is too much noise for any user who only wants to quickly sift through data and do what must be done. With Obcerv, you can group events together, search for affected entities, and look for entities that produce the most noise. Identifying and reconfiguring rules and thresholds can help reduce the alert fatigue.

For more information, see Alerting.

Automated notifications Copied

I need to be notified of important alerts in my existing tool.

You do not need to be logged in and constantly reviewing the Obcerv Web Console to get notified when your IT estate needs attention. With integration into third-party tools such as Slack, you can receive important messages in your preferred channel. Depending on your requirements, you can track a single entity or a group of entities and set triggers for when severity thresholds are breached.

For more information, see Notifications.

Centralized dashboarding Copied

Other teams need to see the Key Performance Indicators from my applications health.

With Obcerv, you can create and easily share native and Grafana dashboards with others. Since both logs and metrics are available in the same dashboard, more users can appreciate the rich sets of data and insights from Obcerv. Obcerv forecasting algorithms can also analyse historic data for future predictions. This in turn opens up possibilities for answering questions such as: what does normal behaviour look like? When will the threshold break? What can a single metric forecast for the day or the week?

For more information, see Dashboards and Grafana app.

Signal forecasting Copied

Predictive forecasts, such as if my disk usage will go over 90% in the next 2 hours, are crucial to managing my IT operations.

Make informed decisions with ready access to usage-based and real-time forecasts on any metric of interest. The Forecaster app generates signals following the thresholds you have set. You will get relevant alerts when there is a probable or ongoing breach: choose a set of servers and a metric, select a lookback period, and then specify warning thresholds. The most suitable forecast model is automatically selected for the observed metric.

For more information, see Overview.

Capacity Planner integration Copied

I need to efficiently get all my monitoring data into Capacity Planner to help me identify savings or potential outages.

With one-click sync from Obcerv, you can send all relevant data to ITRS Capacity Planner. Through the Capacity Planner app, you can easily publish data (such as CPU, memory, and disk utilisation) from Obcerv to a Capacity Planner instance. Obcerv boosts capacity planning capabilities by aggregating multiple data sources, such as Geneos, OP5 Monitor, AWS EC2, Kubernetes nodes, and Azure virtual machines.

For more information, see Capacity Planner app.

Interoperable data storage and access Copied

Open-source tools only provide one piece in the storage system we need.

Since Obcerv is deployed in a Kubernetes platform, you can take advantage of the extensible nature of Kubernetes to easily install, expand, and manage deployment. The Obcerv platform uses scalable APIs at both ends–from sending data into Obcerv to exporting data for consumption. Obcerv gives you more control to decide on which data to publish to the platform. This lets you manage the scale of data storage vis-a-vis the huge amount of data collected from monitoring.

For more information, see Data Model.