u/conor-robertson

A few weeks back I mentioned I was working on something for the sandbox. It's live now: you can drop in your own CSV and query it directly in the browser!

The bit I care most about: your file never leaves your browser. The whole thing runs on DuckDB compiled to WebAssembly, so the bytes go from the file picker straight into the tab's memory and vanish when you close it. Nothing is uploaded, nothing is stored, nothing is logged. That was a deliberate design line rather than a nice-to-have.

What you can do with it:

• Drop in a .csv (20MB per file) and it becomes a queryable table straight away
• Write whatever SQL you like against it, with the schema panel showing your columns and types
• Chart the result of your query: pick your x and y, an aggregate, a series to split by, sort and limit
• Date columns get a grain toggle, so you can roll the same query up by day, month or year without rewriting it
• Export the chart as a PNG at 2x scale, which is good enough to drop into a slide or a post
• Export your result set as CSV.

There are also some ready-made datasets in there if you don't have anything to hand and just want to practise: IMDB, Spotify, NBA, Steam games, a crime dataset and a couple of others.

It's live at querycase.com/sandbox. Genuinely keen for feedback on the chart builder in particular, since that's the newest part and I've been staring at it too long to see it clearly. If it does something annoying, tell me and I'll fix it 😄

Fair warning up front: this is very much a testing phase. It works, but I have been rebalancing it all week and there will be rough edges.

It's a Wikipedia race: you get a start article and a target, and you click links to get there. The twist is that arriving isn't enough on its own. Every hour has a score goal, and you build a deck between rounds to hit it: tabs that multiply your score for opening certain kinds of page, snacks you can burn mid-run when it's going badly. Very much inspired from Balatro.

A full run is one night, 10PM through to 5AM, eight rounds, and it gets mean around 3AM. There's also a daily run if you just want a two minute thing.

It's all real Wikipedia, live, so the pages are whatever they happen to be today. Which does mean it occasionally sends you somewhere ridiculous.

Free, in the browser, no signup, no ads, nothing to buy. Side project, bit of fun, not selling anything.

https://wikirogue.com/

Genuinely after feedback & any ideas to make it better 😄

One of the most common questions I see in this subreddit is "what projects should I build?" and the answer is: something with a clear question, real data, and a shareable output.

So I built a walkthrough for exactly that. It uses two completely free tools:

1. QueryCase's IMDb Sandbox - 250 of the highest rated films on IMDb, queryable in your browser with no setup. You write three SQL queries to answer different questions about the data.
2. Tableau Public - free, runs in browser, gives you a shareable link when you publish.

The three queries cover the core of analytical SQL that actually shows up in interviews and jobs: multi-table JOINs, GROUP BY with aggregation, HAVING, subqueries, and derived columns. Each one is explained line by line so you understand why it works, not just what it does.

The finished dashboard has KPIs, a films-by-decade bar chart, an acclaim vs popularity scatter, top genres by average score, and an actors leaderboard.

The starter dashboard is just that. The version worth putting on your CV is the one you take further. Add a director leaderboard. Build a decade filter so the whole dashboard slices interactively. Rework the layout and colours until it genuinely looks better than mine. Write a fourth query that answers something the data hasn't told you yet.

"I built this and then pushed it further" is a far stronger thing to say in an interview than "I followed a tutorial."

Full walkthrough here with every query written out and all the Tableau steps:

https://querycase.com/blog/build-an-imdb-dashboard-with-sql-and-tableau

Happy to answer questions about the SQL or the Tableau side if anyone gets stuck.

Built QueryCase solo over a few months and it's been live for just over two weeks now.

The pitch: SQL tutorials teach syntax fine but there's never a reason to care about the answer. You filter a fake employees table, get a result, close the tab, forget it by Thursday. So instead of exercises, you solve detective mystery cases. Real case briefing, real database, real SQL to crack it.

54 cases, five detective ranks, timed exams with shareable certificates, a free Sandbox with real datasets (IMDB, Spotify, NBA, Steam, Pokémon), and a no-hints Investigations mode for pressure. Runs entirely in the browser via DuckDB WASM.

On the business side: went with a one-time payment (£14.99) instead of subscription after running the numbers - most users finish the core path in 2-3 months, so lifetime value ends up roughly the same either way, minus the churn and refund overhead.

350 signups and 1,500 cases completed so far, mostly from Reddit and organic search, no ad spend.

Also just put it up on Product Hunt today if anyone wants to see the full thing:

https://www.producthunt.com/posts/querycase/maker-invite?code=AMxgTN

Happy to talk through the pricing decision, the DuckDB build, the numbers, anything.

Hey r/ProductHunt - just launched QueryCase today after building it solo over the past few months.

The idea: SQL tutorials teach syntax fine, but there's never a reason to care about the answer. You filter a fake employees table, get a result, close the tab, forget it by Thursday.

QueryCase flips that. You're a detective. You get a real case briefing, a real database, and you write actual SQL to solve a mystery. The JOIN matters when a suspect has an alibi.

54 cases, five detective ranks, timed exams with shareable certificates, a free Sandbox with real datasets (IMDB, Spotify, NBA, Steam, Pokémon), and a no-hints Investigations mode for when you want pressure. Everything runs in the browser via DuckDB WASM, nothing to install.

It's live on Product Hunt today if you want to take a look: https://www.producthunt.com/p/querycase/querycase

Genuinely happy to answer anything - the build, the DuckDB implementation, the gamification decisions, why I went with a one-time payment instead of subscription. First launch, so any feedback is welcome too.

Window functions come up here constantly, and they're the wall a lot of people hit right after they're comfortable with SELECT, WHERE and JOIN. The syntax looks alien and most explanations open with "frames" and "partitions" and lose people immediately.

The idea underneath is much simpler than the wording suggests. A window function does the same calculation as GROUP BY, but instead of crushing each group down to one summary row, it keeps every row and writes the answer in a new column beside it. That's the whole trick. Running totals, rankings, "this row vs the previous one" are all just an aggregate that didn't collapse.

The clearest way to see it is side by side:

• GROUP BY squad → 2 rows. One line per squad, the individual names are gone.
• SUM(xp) OVER (PARTITION BY squad) → 5 rows. The same squad total, written next to every member, all five rows kept.

Same totals, different row counts. That gap is the entire concept.

A few things worth knowing once that lands:

• ROW_NUMBER / RANK / DENSE_RANK look identical until there's a tie. ROW_NUMBER never ties (1,2,3,4). RANK ties then skips (1,2,2,4). DENSE_RANK ties without skipping (1,2,2,3).
• The ORDER BY inside OVER() steers the calculation (what counts as "above" for a running total). The ORDER BY at the end of the query just sorts the output. Different jobs.
• You can't filter on a window function in WHERE, because the rank isn't worked out yet when WHERE runs. Compute it in a CTE first, then filter. That wrap-then-filter shape is behind almost every "top N per group" query.

I put together an interactive version that shows all of this, a live leaderboard where you pick the function, toggle PARTITION BY and ORDER BY, and watch the column change without losing any rows. You can tap any computed value to highlight the exact rows it was built from: https://querycase.com/blog/sql-window-functions-explained

If you teach this or learned it recently, I'd love to know: is there a window function concept you wish someone had shown you visually that's missing? And are RANK vs DENSE_RANK and the top-N-per-group pattern explained clearly, since those are the two that seem to trip people up most?

Hey everyone! 👋

I’ve been working on QueryCase, a gamified SQL learning platform where users learn SQL by solving interactive detective-style investigations and realistic business scenarios rather than following traditional tutorials.

The platform has been live for just over a week and has already reached 200 users, which has been exciting (and slightly terrifying!) as it’s my first ever project.

I’m planning a Product Hunt launch next week and have put together a short launch video. I’d love any feedback on the video, landing page, messaging, or first impressions before I launch.

Any thoughts would be hugely appreciated - thanks in advance!

The reason most people bounce off SQL tutorials isn't that SQL is hard. It's that nothing they're querying matters to them to keep them motivated!

You spend the first week writing SELECT name FROM employees WHERE dept = 'Sales' and technically you've written SQL. But there's nothing to discover, no reason to care whether you got it right, and no motivation to open the app tomorrow.

The fix isn't a better tutorial. It's querying data you actually care about.

Wrote a full breakdown on this including a learning framework, the six SQL concepts that cover 90% of real work, and how to pick the right dataset to keep yourself motivated: querycase.com/blog/how-to-learn-sql-fast

One of my first blogs so based on my own experience - happy to hear if there's anything you'd do differently or any other recommendations you have to improve SQL learning! 😄

I've been in data analytics 7 years and the thing that separates people who get offers from people who don't usually isn't technical ability.

Before any interview, spend 30 minutes thinking about the company. What do they do? How do they make money? What does their data probably look like? A SaaS company lives and dies by retention, churn and engagement. An e-commerce company cares about repeat purchase rate and basket size. Walk in knowing that language and using it naturally.

But more than that, think like you already work there. If they ask you to investigate a churn spike, don't just write a query. Ask questions first. Has anything changed recently? Any new experiments running? Product updates? Pricing changes? That's what a real analyst does on day one and it's exactly what interviewers want to see.

The candidates who stand out aren't always the fastest at writing SQL. They're the ones who slow down, ask the right question, and make the interviewer feel like they're already thinking about the same problems.

Wrote a full breakdown on this including domain prep guides for different industries and a pre-interview checklist: querycase.com/blog/sql-interview-questions

One of my first blogs so based largely on my own experience in the industry. If there's anything critical I've missed or you'd do differently I'd genuinely love to hear it in the comments.

I've spent the last few months building something and I'm finally at the point where I want to share it properly rather than just quietly hoping people find it.

The idea came from a frustration I kept seeing (and feeling myself): SQL tutorials teach the syntax fine but there's never a reason to care about the answer. You filter a table called employees, get a result, and nothing happens. Your brain doesn't bother keeping it.

I wanted to try a different approach. QueryCase teaches SQL through detective investigations. You get a briefing from Chief Fox (our mascot), a real database to query, and a mystery to crack. The JOIN matters when a suspect has an alibi. The WHERE clause matters when you're trying to find who entered the building at 22:13. The SQL is the tool for solving something, not the point in itself.

Here's what's actually in it:

• A structured learning path across 54 cases, going from Recruit through Rookie, Detective, Senior Detective, and Chief Detective. Each rank has drills and a level exam to pass before you progress.
• Sandbox mode where you can explore real datasets (IMDB movies, Spotify, sports stats, Steam games) and run whatever you want with no pressure and no mystery attached. Just free exploration against actual data.
• Everything runs in the browser using DuckDB WASM so there's nothing to install.

I'm a solo developer and this is genuinely early days. I'm sharing here because this community is exactly the kind of people I built it for, and I'd rather get honest feedback now than find out later I've built the wrong thing.

What's missing? What would make you actually stick with something like this versus what you've used before?

querycase.com if you want to take a look.

Any feedback appreciated!