What Is Open Source Monitoring?


Open source monitoring is the continuous open source management of sensors, IoT devices, networks, servers, applications and more. Developers can create a robust and flexible framework within these open source monitoring tools to capture the continuous flow of data and processes within.

Essentially, open source monitoring is allowing businesses to manage their entire network architecture, from one end to another. The infrastructure can then be remotely managed by providing tiered access to stakeholders. Data can be collected from a variety of inputs, and written into a database directly using an open source server agent.

Stacks can also be integrated into the architecture, without requiring manual intervention to ensure streamlined connectivity. Open source monitoring is emerging as an industry standard, as more companies opt for its multitude of benefits and advantages.

Advantages of continuous open source monitoring

There are several advantages of opting for continuous monitoring of the network.

Complete automation of management: Automation allows systems administrators to monitor the entire architecture without intervening in any single node. Sensor information is directly fed into the monitoring tool, allowing teams to analyze the data remotely.

Simplifying complex processes: One of the major advantages of implementing an open source monitoring system is to simplify complex data capturing processes. Developers can design a robust architecture inside the system and obtain information directly from sensors and devices.

Storing large datasets effectively: Open source monitoring tools are adept at storing large amounts of time-series data as inputs inside coherent databases. These tools allow you to write into these databases directly, all the while continuously monitoring the states of your devices.

Triggered alerts and messages: Project managers can set triggered alerts for specific instances, or when a critical juncture has been reached. These alerts can be extremely helpful, especially when it comes to the monitoring power supply or sensor performance.

Unification of system architecture

The ideal open source monitoring solution enables the complete unification of systems architecture. It allows admins to review information coming in from all sensors across the network. It also creates a more robust approach for coders to experiment on and receive immediate feedback from the system.

It’s also important to have the necessary controls to remove bottlenecks, reduce server load or to add new components to a network. An open source monitoring dashboard allows system administrators the tools to monitor key areas such as availability, uptime, and response of each component. E.g. an IoT sensor can be remotely updated through the unified systems architecture.

The importance of stack integration

When it comes to monitoring open source stacks, it’s important to use a robust tool that can integrate with multiple platforms. The monitoring tool should have the right API systems in place to pull in information from a wide range of platforms. Whether it’s database connectivity or sensor management, the entire stack needs to be integrated into the dashboard.

Modern open source monitoring tools come with extensive plug-ins and features that make it easier to integrate everything into one coherent platform. Admins can manage server uptime, database availability, application performance, and discovery map loading through a single vantage point.

Deep analytics and rich insights

One of the major reasons why companies integrate open source monitoring tools is to enhance CI/CD pipelines. These projects rely on the continuous flow of data coming in from various sensors and devices. A robust solution needs to be integrated that can channel these data points and make sense of everything.

Machine learning can be introduced here as well, allowing teams to create rich insights from the incoming datasets. Monitoring tools come with robust features that allow for iterative testing to enable more successful experimentation on these data points. Applications can then be enhanced and made that much better through deep analytics.

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