This article covers the third step in the D-BART method, a structured approach to managing SQL Server environments.
What’s in it for you?
Automating well-defined SQL Server tasks brings both immediate and long-term value:
- Consistency: Tasks are performed the same way every time, reducing configuration drift and human error.
- Cost reduction: Time-consuming manual tasks are replaced with efficient, repeatable processes, allowing your team to manage more with fewer resources.
- Scalability: Automation allows operations to scale together with your infrastructure without a linear increase in effort or staffing.
Automation is where the investments made during Detect and Blueprint start to pay off. It’s the turning point where efficiency, consistency, and scale become tangible. But let’s be clear: for me, automation only works when it’s built on a strong foundation.
If you don’t know which servers require a cumulative update (CU), or if your process isn’t clearly defined, automating the task won’t save you, it may even introduce new risks.
So, before moving into the Automate phase, you must ensure two things are in place:
- A reliable inventory to identify your targets (Detect)
- A standardized process to follow step by step (Blueprint)
Skip these, and automation becomes guesswork. Build on them, and it becomes a force multiplier. I believe that nearly everything can be automated. And I said “nearly” just to be safe. 😊
My core principles for reliable automation.
Over the years, I’ve learned a few lessons the hard way, and these are the principles I stick to:
Centralized variables in an XML file.
Store shared variables in a centralized XML file. This approach ensures:
- Consistency: variables are identical across scripts
- Easy maintenance: Update once, update everywhere
- Multi-environment support: Maintain one code-base across multiple domains (e.g., IT and OT) by simply adjusting the variable content.
Structured logging is essential
Every script execution should generate its own log file. Why? Because sooner or later, something will break. When it does, you’ll want to know exactly what happened.
Each log entry is timestamped and classified into clear levels:
- Fatal: Execution stops immediately. e.g., the target SQL Server is offline during database creation
- Logical: A deviation from expected behavior. Execution stops immediately. e.g., expecting one object, but retrieving none
- Non-fatal: An error occurs, but the script continues. e.g., a preferred domain controller is unavailable, but a fallback succeeds
- Informational: Routine status updates. e.g., a database has been successfully created
Invest in error handling
Good error handling isn’t overhead, it’s prevention. And it goes beyond just wrapping code in try/catch blocks.
Start early: check for problems before they happen. For example, verify that a database doesn’t already exist before attempting to create it.
Combine proactive checks with structured error handling, and you’ll spend far less time debugging.
When logging and error handling are done right, you’ll spend far less time troubleshooting.
Script nice or script twice.
Every shortcut you take today is a problem you’ll revisit tomorrow. Avoid the temptation to “just get it working.” Cutting corners leads to fragile scripts and wasted time down the line.
- Test thoroughly
- Never assume.
- Validate edge cases.
- Add comments, explain what you did to future-you (or the next person).
Keep your code clean and maintainable by breaking it down into small pieces that align with each step in your blueprint.
Start small, grow steadily
You don’t need to automate the entire process at once. Begin with:
- The most time-consuming manual steps
- Tasks prone to human error
Over time, expand coverage until the full process is scripted.
Conclusion: Build it forever or fix it forever.
Automation is a force multiplier which unlocks speed, consistency, and control at scale. But don’t skip the prep.
To succeed:
- Invest in a reliable inventory (Detect)
- Standardize your processes (Blueprint)
- Centralize your variables in a separate file outside your scripts
- Have a different log file for every script
- I haven’t had a case yet where I thought I logged too much
- Invest in error handling
- Quality of code will pay off massively over time
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