What are the best practices for writing SQL documentation and comments?

Here's a quick guide: 1. Keep It Short: Make your comments brief but valuable. Please be sure to stick to what's most important. 2. Clear Language: Use simple words that everyone can understand. Avoid jargon or shortcuts that can confuse people. 3. Stay Consistent: Keep your style the same—like how you capitalize or indent—so your code is easier to read. 4. Descriptive Names: Use names for tables or variables that tell you what they're for. This makes the code self-explanatory. 5. Be Organized: Use headings or bullet points to separate different sections or ideas. It makes it easier to find what you're looking for. 6. Keep Updating: Regularly review and update your code and comments to fix errors and keep things up-to-date.

During my early days in data analytics, I was handed a script where every line had a comment. It felt like navigating through a jungle! Through experience, I realized: commenting is an art. It's less about dictating every move and more about offering a clear roadmap.

I've learned that using comments sparingly in SQL code is helpful, but I don't entirely agree with always using as few comments as possible. Sometimes, adding well-placed comments can make a big difference. Think of them like labels on puzzle pieces – they clarify complex code. For instance, imagine a detailed recipe with extra explanations – it's easier to follow. So, my suggestion is to use comments strategically when your SQL is like a complex puzzle or recipe. Comments are like helpful notes, simplifying understanding.

At some point you will encounter the famous "leading vs lagging" comma argument (placing the commas before column names or after them). You will hear and read arguments for both approaches but ultimately which method you choose is personal but most important is that you are consistent with this choice in your documentation.

One thing I found helpful is how it highlights that improving your SQL documentation isn't just about fixing mistakes. It's more like a sign of growth as a data analyst. Think of it as upgrading your skills over time. You might think you know everything about a piece of code, but later, you might see new ways to make it work better. It's like how an artist keeps refining their masterpiece. Your growing knowledge can bring fresh insights and make your code smarter and more useful. This idea of always getting better isn't just a good practice; it's a way of thinking that helps you succeed in the changing world of data analysis.

Regularly revisiting and refining your SQL documentation isn't just about fixing errors; it's a testament to growth as a data analyst. Over time, your evolved perspective can bring newfound clarity and optimization to even the most familiar code.

Though you can document some things in the SQL code, don't fall for the "documentation is the code" trap. Though commenting is critical to help the code maintainability, it usually fails to explain the business context adequately. Like comments, any documentation should be done wisely. Too much irrelevant information can be just as bad as too little documentation.

Write a code that speaks: 1.Intuitive Code: There were times I stared at code, wondering its purpose. I aim to write so the next person doesn't have to wonder. 2.Prioritize Brevity: Short comments often hit the hardest. They save time and convey just as much. 3.Avoid the Jargon: In meetings and in code, plain language always wins. It's universal and reduces the learning curve. 4.Meaningful Naming: I've always found that when tables or variables have clear names, half my job is done. 5.Organize and Structure: An organized code layout saves so much time. It's direct and easier to navigate. 6.Regular Reviews: I set aside time to revisit and update my codes. It's about staying relevant and efficient.