One idea
Good SQL starts before SQL. First write the product question so clearly that another person can build the same query.
A clear question names the metric, who it is about, the event or table, the time period, and how the result should be grouped.
Why this matters
Most bad analytics projects do not fail because of syntax. They fail because the question is vague.
“How is our app doing?” can mean signups, payments, retention, active users, or revenue. Each needs a different query and a different answer.
Question checklist
Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.
0 of 5 done
Make it queryable
Here is a weak question and a better version for a campus food ordering app.
The better version is ready for SQL because it tells you what to count and how to group it.
Weak: Are users ordering food?
Better: For users in Bengaluru, how many paid orders were placed each day from 1 Jan 2026 to 7 Jan 2026?
Metric: paid orders
Rows included: orders where status = 'paid'
Time range: 2026-01-01 to 2026-01-07
Group by: order date
Filter: city = 'Bengaluru'Use AI well
You can ask ChatGPT, Gemini, or Claude to improve your question before writing SQL. Do not ask it to guess your business rules.
Paste your rough question and ask: “Rewrite this as a SQL-ready analytics question. List metric, filters, date range, grouping, and assumptions.” Then check every assumption yourself.
Avoid hidden definitions
Words like active, retained, converted, paid, and new look simple but need rules.
For example, “active user” could mean opened the app, viewed a product, placed an order, or paid. Pick one definition and write it down.
Start a question log
Your final project will need a short explanation of insights. A question log makes that easier.
Create a plain file in your project folder. Add one row for every question before you write the query.
File: questions.md
Question 1
Product question: Are paid orders increasing week by week?
SQL-ready question: Count paid orders by week for Jan 2026.
Metric: paid orders
Table needed: orders
Filters: status = 'paid'
Time range: 2026-01-01 to 2026-01-31
Group by: week
Why it matters: Shows whether demand is growing.
Output expected: week, paid_ordersDo this now
Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.
0 of 5 done