He was just an ordinary product manager, struggling with messy data and failed queries. Day after day, he was constantly thwarted by the complexities of SQL. Queries were unsolvable puzzles—joins, aggregations, and null values tripping him up at every turn. Then, one fateful afternoon, everything changed. He discovered a secret weapon against his SQL struggles—LLMs. With AI by his side, he could finally conquer those inscrutable queries and turn ideas into insights with confidence.
And he became… Super SQL Man!
This is his story.
“Have any of you ever written SQL code as a product manager?” asked the leader of the product meet-up. He continued, “It’s on every job description I’ve seen, but I’ve never seen a product manager write SQL from scratch.”
I laughed, because I’ve been there. SQL seems deceptively simple—just ask the database for what you need, right? But once you dive in, it’s anything but. Unlike procedural programming, SQL requires you to think differently—focusing on what you want rather than how to get it. And the challenges don’t stop there. Joining tables, ensuring data integrity, handling missing values—it’s all too easy to end up with inaccurate results, duplicates, or painfully slow queries.
The last time I tried writing SQL code was five years ago. I had a clear vision of what I wanted, and I figured, “I used to code; I can do this.” Four hours later, after wrestling with Microsoft Access, I had nothing to show for it. It was like trying to negotiate a business deal in a foreign country with nothing but a phrasebook—sure, I could order a coffee, but closing the deal? Not a chance. In the end, I sketched out my ideal table structure in Excel, handed it to a developer, and watched them produce the query in five minutes.
I felt like a kid at school, showing up with oversized crayons and a giant pad of paper—excited but incredibly clumsy. I’d scrawl out my ideas in broad, wobbly strokes, proudly declaring, “I want this!” Then someone more competent would take one look, offer a reassuring nod, and effortlessly translate my messy masterpiece into a precise, elegant SQL query. Once the hard work was done, I’d confidently step in to modify the SELECT statements to get the data I needed.
But now, with LLMs like ChatGPT, everything is different.
It’s as if I’ve been handed a translator—one that not only understands my crayon sketches but turns them into masterpieces, instantly. I can describe what I need in plain language, and instead of second-guessing every join, filter, and aggregate, I get back a fully formed query that actually works. It’s liberating. SQL, once a frustrating roadblock, has become an accessible tool I can finally wield with confidence.
As an example, say I’m running an e-commerce site that ships products. Each order moves through different statuses—Ordered, Shipped, and Delivered—all tracked in a table that has the columns status_id, product_id, status, and start_time.
Order Status History
| Column Name | Description |
| status_id | A unique identifier for each status record. |
| product_id | The identifier for the product associated with the status. |
| status | The current status of the product (e.g., “Ordered,” “Shipped,” “Delivered”). |
| start_time | The timestamp marking when the product entered the given status. Note there is no finish time. |
At some point, I realize I need to analyze how long products spend in each status. Sounds easy enough, right? Just subtract timestamps. But once I dig in, things get complicated.
I only have the start date. How do I get the finish date?
What about orders still in progress?
How do I keep calculations accurate across multiple orders?
And there I am again, crayons in hand, sketching out arrows between statuses and convincing myself it’s just simple math.
But this time, I have a secret weapon. I plug the following into ChatGPT:
“I have an order tracking table with the columns status_id, product_id, status, and start_time. I want to calculate the number of days between each status transition for each order. Write a SQL query to pull it back.”
And in an instant, I get this:
WITH StatusTransitions AS (
SELECT
product_id,
status,
start_time AS status_start,
COALESCE(
LEAD(start_time) OVER (PARTITION BY product_id ORDER BY start_time),
CURRENT_DATE()
) AS status_end
FROM order_tracking
)
SELECT
product_id,
status,
status_start,
status_end,
DATEDIFF(status_end, status_start) AS days_in_status
FROM StatusTransitions
ORDER BY product_id, status_start;ChatGPT handled all the complexities seamlessly, knowing how to solve all these problems. Here’s how it did it:
I only have the start date. How do I get the finish date?
It used the LEAD window function to retrieve the start_time of the next row for the same product_id, providing a clean transition to the next status.
What about orders still in progress?
For orders without a next status, it incorporated the COALESCE function to replace NULL values with the current date—ensuring no unfinished orders fall through the cracks.
How do I keep calculations accurate across multiple orders?
It applied PARTITION BY product_id to ensure each order’s statuses were tracked independently, preventing data from overlapping or getting mixed up.
It can also explain SQL queries. If I ask my trusty sidekick to explain the query above, I get back:
- High-level overview: The query calculates how many days a product spends in each status by determining the start and end times for each transition, organized chronologically for each product.
- Why it’s helpful: The query ensures accurate tracking of time spent in each status, even for statuses still in progress.
- Query details: The WITH clause creates a temporary table to calculate status_start and status_end, using LEAD to find the next status’s start time and COALESCE to handle missing end times by substituting the current date. The main query calculates the difference in days between status_start and status_end with DATEDIFF, sorting the results by product_id and status_start.
And that’s how I became Super SQL Man! Faster than a query optimizer, more powerful than a recursive join, able to SELECT across vast datasets in a single bound! Once, as a mere mortal product manager, I stumbled through SQL, tangled in a web of missing keys and bewildering errors. But with my AI-powered sidekick, I now navigate the data landscape with ease—solving business problems one query at a time. Stay tuned, data citizens, because Super SQL Man always gets the answer!
What once felt impossible—writing SQL with confidence—now feels within reach. No more staring at blank query editors or relying on others to make sense of my data. I focus on the questions that matter and let AI handle the technical details. It’s not about mastering every SQL function, but about having the right tools to turn ideas into insights.
If a humble product manager with a crayon and a dream can become Super SQL Man, so can you.
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