For the last several weeks, we’ve been discussing the four types of automation. First, we outlined the different types and how zipping together provisioning and configuration automation make them more effective. This week we’ll dive deep into monitoring data automation.
This type of automation is the most essential element for building successful long-term systems. However, it is often the most overlooked. Building productive, reusable automation of any type, requires monitoring, data collection, and discovery.
Defining Monitoring Data Automation
When we talk about monitoring data automation, here’s what we mean:
Monitoring Data Automation automates a collection of data that is required to discover the state of a system before other automation tasks can be run.
Monitoring data automation unlocks several fundamental automation tasks:
- The ability to provide appropriate data into systems so secure and sensitive data can be moved out of systems.
- Confidence to send data back in at the appropriate time, without over-provisioning systems because we didn’t know what was going on in the environment.
Why Monitoring Data Automation Is Critical
If monitoring data isn’t being collected its a good bet automation rules are being applied blindly. Automating this data collection and incorporating it into a broader automation story enables dynamically collected system information to influence and change orchestration operations.
This data can also be used to make sure that automation scripts are doing the right things for the system. Data collected about a system’s state can be used to ensure automated rules or changes only get applied to systems in an expected state. This is an evolution from running automation, hoping that it works, and dealing with the fallout if it doesn’t.
Check Your Assumptions
Automation has built-in assumptions. If the assumptions don’t match, then the automation should stop. This gives operators a chance to correct any underlying issues. Automation that doesn’t stop often leaves systems in a worse state.
Conformance and Compliance
Another benefit of monitoring data automation is conformance and compliance. It is critical to be have a way to verify your systems are safe and secure. You must have a way to check that operations performed as expected. It is also important to have a way to prove that you have conformed operations to your standards and policies in a consistent, repeatable way.
Even if it is legally mandated, compliance provides transparency in operations and certainty that you’ve actually built the system you think you’ve built.
The industry needs to shift from thinking that this collecting monitoring data is intrusive to enterprise IT environments. In actuality, secure, resilient systems cannot be built without this type of data transparency. Automating and standardizing systems, based on real-time data, must become essential operations in every environment.
RackN makes monitoring data automation is a first class citizen in Digital Rebar’s method of building infrastructure pipelines. Everything Digital Rebar automates revolves around the idea that systems can be inspected, checked, and validated. Digital Rebar’s automation will assess, correct, or stop if discovered information doesn’t match what the expectations.
Digital Rebar brings real insight into what your systems look like before, during, and after any configuration or provisioning process. That data feeds a much tighter, more robust automation. We think this is a core component of any resilient infrastructure.
Why not try Digital Rebar for yourself to power of monitoring data automation?