Writing Maintainable ETL Code: Empowering Support and Avoiding Knowledge Silo

Writing Maintainable ETL Code: Think Beyond Just the Developer When we write ETL code, our focus is usually on business logic, data accuracy, and performance. And while these are critical, there's another equally important aspect we often overlook: maintainability. A piece of ETL logic that works perfectly today may still cause friction if it's written in a way that's hard to understand, troubleshoot, or modify. Remember: you’re not just writing code for yourself—you’re writing it for the support team, the on-call engineer, and the next developer who picks it up. Let’s walk through why writing clear, maintainable ETL code matters and how to do it right. Why Maintainability Should Be Your Priority Imagine your ETL job fails at 3:00 AM. The support team gets paged. They open the job and see a massive, undocumented graph full of layers, nested transforms, and custom scripts. No comments. No hints. No clear flow. What do they do? Most likely: escalate it. That means the issue gets pushed back to development or even the original resource who built it—which defeats the purpose of having a 24x7 support model. You’ve not only slowed down resolution, but you've also increased business impact due to the delay. This is where clarity beats cleverness. The Hidden Cost of Not Sharing Knowledge Unfortunately, many developers write complex ETL code but don’t take the time to share knowledge or document their work. This creates unnecessary dependencies on a few key people. If those people are unavailable—due to vacation, resignation, or even a simple illness—the entire system can come to a halt. This knowledge silo is a major risk for any company, potentially leading to prolonged outages or costly firefighting. Thankfully, we’re entering a new era. With advancements in AI, tools are now available that can scan, interpret, and explain complex code automatically. This means that even if documentation is sparse, AI can help bridge the gap—empowering support teams and new developers to understand ETL workflows faster than ever before. Still, relying solely on AI isn’t a substitute for good documentation and clear code. AI is a powerful assistant—but the best practice remains to write clean, understandable code and share knowledge proactively. Keep It Simple, Even When It’s Complex ETL workflows can be inherently complex, especially when dealing with multiple systems, business rules, and dependencies. But complexity in function doesn't have to mean complexity in code. Here are a few principles to follow: 1. Design for the Reader Write your ETL code as if someone who didn't build it will have to debug it at 2:00 AM. That’s your audience. Use meaningful names for graphs, components, and parameters. Avoid overly compact or “smart” solutions that save you lines but confuse others. Group components logically and use layout/annotations to show the flow clearly. 2. Document Where It Matters If your logic involves conditional branching, transformation rules, or shell scripts, take the time to document them. Add comments directly within the graph or script blocks. Maintain a README-style document that outlines key design decisions. For reusable graphs or wrappers, explain inputs, outputs, and expectations. 3. Provide a Runbook for Every Job A runbook is a goldmine for the support team. It tells them what to do when things go wrong, without needing to involve dev. A good ETL runbook includes: Purpose of the job (why it exists) Schedule (when it runs) Upstream and downstream dependencies Common failure scenarios and how to handle them How to re-run it safely (commands or steps) Alert contacts if escalation is needed This gives the support team confidence and autonomy. When You Must Write Complex Logic… Sometimes, you just can’t avoid complexity—maybe it’s a regulatory requirement or a cross-platform orchestration. In those cases: Break the logic into smaller, modular graphs or scripts. Include a high-level design doc with visuals or flowcharts. Walk through it with your support team during handover. If you can’t simplify the code, make the understanding of it simple. Final Thoughts Your job as an ETL developer isn’t just to make the data flow. It’s to make sure that the system keeps running—even when you’re not around. Maintainable code, proper documentation, and a thorough runbook are not “nice-to-haves.” They’re part of doing the job right. Support teams are your partners in production. Set them up for success, and you’ll have fewer escalations, fewer late-night calls, and more time to focus on building new things instead of fixing old ones. And remember: with the rise of AI-powered tools, the future looks promising for breaking down knowledge silos. But until then, the best practice remains clear—write clean code, document well, and share knowledge openly.

May 17, 2025 - 01:26

Writing Maintainable ETL Code: Empowering Support and Avoiding Knowledge Silo

Writing Maintainable ETL Code: Think Beyond Just the Developer

When we write ETL code, our focus is usually on business logic, data accuracy, and performance. And while these are critical, there's another equally important aspect we often overlook: maintainability.

A piece of ETL logic that works perfectly today may still cause friction if it's written in a way that's hard to understand, troubleshoot, or modify. Remember: you’re not just writing code for yourself—you’re writing it for the support team, the on-call engineer, and the next developer who picks it up.

Let’s walk through why writing clear, maintainable ETL code matters and how to do it right.

Why Maintainability Should Be Your Priority

Imagine your ETL job fails at 3:00 AM. The support team gets paged. They open the job and see a massive, undocumented graph full of layers, nested transforms, and custom scripts. No comments. No hints. No clear flow.

What do they do? Most likely: escalate it.

That means the issue gets pushed back to development or even the original resource who built it—which defeats the purpose of having a 24x7 support model. You’ve not only slowed down resolution, but you've also increased business impact due to the delay.

This is where clarity beats cleverness.

The Hidden Cost of Not Sharing Knowledge

Unfortunately, many developers write complex ETL code but don’t take the time to share knowledge or document their work. This creates unnecessary dependencies on a few key people. If those people are unavailable—due to vacation, resignation, or even a simple illness—the entire system can come to a halt.

This knowledge silo is a major risk for any company, potentially leading to prolonged outages or costly firefighting.

Thankfully, we’re entering a new era. With advancements in AI, tools are now available that can scan, interpret, and explain complex code automatically. This means that even if documentation is sparse, AI can help bridge the gap—empowering support teams and new developers to understand ETL workflows faster than ever before.

Still, relying solely on AI isn’t a substitute for good documentation and clear code. AI is a powerful assistant—but the best practice remains to write clean, understandable code and share knowledge proactively.

Keep It Simple, Even When It’s Complex

ETL workflows can be inherently complex, especially when dealing with multiple systems, business rules, and dependencies. But complexity in function doesn't have to mean complexity in code.

Here are a few principles to follow:

1. Design for the Reader

Write your ETL code as if someone who didn't build it will have to debug it at 2:00 AM. That’s your audience.

Use meaningful names for graphs, components, and parameters.
Avoid overly compact or “smart” solutions that save you lines but confuse others.
Group components logically and use layout/annotations to show the flow clearly.

2. Document Where It Matters

If your logic involves conditional branching, transformation rules, or shell scripts, take the time to document them.

Add comments directly within the graph or script blocks.
Maintain a README-style document that outlines key design decisions.
For reusable graphs or wrappers, explain inputs, outputs, and expectations.

3. Provide a Runbook for Every Job

A runbook is a goldmine for the support team. It tells them what to do when things go wrong, without needing to involve dev.

A good ETL runbook includes:

Purpose of the job (why it exists)
Schedule (when it runs)
Upstream and downstream dependencies
Common failure scenarios and how to handle them
How to re-run it safely (commands or steps)
Alert contacts if escalation is needed

This gives the support team confidence and autonomy.

When You Must Write Complex Logic…

Sometimes, you just can’t avoid complexity—maybe it’s a regulatory requirement or a cross-platform orchestration. In those cases:

Break the logic into smaller, modular graphs or scripts.
Include a high-level design doc with visuals or flowcharts.
Walk through it with your support team during handover.

If you can’t simplify the code, make the understanding of it simple.

Final Thoughts

Your job as an ETL developer isn’t just to make the data flow. It’s to make sure that the system keeps running—even when you’re not around.

Maintainable code, proper documentation, and a thorough runbook are not “nice-to-haves.” They’re part of doing the job right.

Support teams are your partners in production. Set them up for success, and you’ll have fewer escalations, fewer late-night calls, and more time to focus on building new things instead of fixing old ones.

And remember: with the rise of AI-powered tools, the future looks promising for breaking down knowledge silos. But until then, the best practice remains clear—write clean code, document well, and share knowledge openly.