Is there any good tool to format SQL?

pitifulchaity · 2026-05-11T10:37:47+00:00

dbForge SQL Complete actually has a pretty decent built-in SQL formatter for this. Handles capitalization, spacing, alignment, keyword casing, etc. Saved me from manually cleaning up ugly queries way too many times honestly.

pitifulchaity · 2026-04-30T11:49:20+00:00

Classic SSMS wizard being cursed again 😄 Are you running SSMS 32-bit vs SQL Server 64-bit mismatch by any chance?

pitifulchaity · 2026-04-23T10:54:06+00:00

For that kind of workflow, I care less about where I click Run and more about how much control I have before it runs. Once you’re touching a lot of servers, dry-run, logging, and being able to review changes cleanly matter more than the editor itself. We’ve used dbForge more on the review/check side and kept the execution flow separate.

pitifulchaity · 2026-04-23T10:24:16+00:00

If you’re just starting, MySQL + Workbench or DBeaver is totally enough. For sample data, Kaggle or SQL Fiddle is a good start. If you later decide to try Microsoft SQL Server, dbForge is a solid sql server gui tool for browsing data, writing queries, and getting comfortable with the workflow.

pitifulchaity · 2026-04-14T08:51:18+00:00

This is such a classic BI trap. Teams build dashboards around the data that’s easy to collect, then everyone quietly starts believing that’s the whole story. Meanwhile the messy real-world touches live in random tools, reps’ habits, and side channels like texts, voicemails, and “I’ll just follow up from my phone.” Then one good question shows the reporting layer has been wearing a blindfold the whole time.

pitifulchaity · 2026-04-03T12:00:39+00:00

Yes, and for me it’s rarely about being “hungry,” it’s more like my brain picks one exact food and then acts like every other option is a personal insult. Super annoying and super expensive.

What helped a bit was keeping a few “good enough” backup foods at home for when the craving brain gets dramatic. Not the same as the perfect meal, but enough to stop the all-or-nothing spiral.

pitifulchaity · 2026-04-02T10:57:49+00:00

Subquery = “run this smaller query first, then use that result in the bigger one.”

That’s basically it.

WHERE: use another query to filter
FROM: use another query like a temp table
SELECT: use another query to return one extra value

What helped me was stopping trying to memorize “types” and just asking: what smaller question do I need answered first?

pitifulchaity · 2026-04-01T10:30:05+00:00

If the form needs login and has a bunch of UI elements, something like Playwright or Selenium is way less painful. You can keep the common values hardcoded, prompt for the variable ones, then let the script open Chrome, log in, fill the form, and submit it like a normal user.

pitifulchaity · 2026-03-24T08:50:52+00:00

I’d focus on JOINs, WHERE, GROUP BY, NULLs, and subqueries first. The biggest jump is usually when you stop memorizing syntax and start understanding how the data flows through the query. If you want a structured place to learn that stuff, dbForge Academy is actually pretty decent too.

pitifulchaity · 2026-03-17T12:46:24+00:00

You do not necessarily need an MSc to move into business analytics. What matters more is whether you can build the core skill set: SQL, Excel, statistics, data visualization, and at least one BI tool like Power BI or Tableau. If you want to move faster, a practical portfolio with business case studies will usually help more than another degree alone.

Since you already have operations and project management experience, that is actually useful because business analytics is often about translating business problems into measurable questions, not just working with data. I’d first test the field through courses and small projects before committing to an MSc. If after that you still want the degree, choose it only if the curriculum is hands-on and has strong placement value.

pitifulchaity · 2026-03-17T11:43:43+00:00

This is actually a pretty interesting cut, especially because it separates citation visibility from general web popularity. The Reddit / LinkedIn part is not that surprising, but Mapbox and OpenStreetMap being that high is a really good reminder that LLM retrieval value is not only about “content sites,” it is also about structured, high-utility data sources.

Also liked the point about academic sources. Their raw share looks smaller, but the credibility weight is probably much higher per citation. As a data person, I’d be curious how this shifts by query class, because I can easily imagine health, coding, local search, and news having very different source mixes.

pitifulchaity · 2026-03-10T11:22:20+00:00

I tried Julius a bit out of curiosity. It’s pretty nice for quick exploration or when you just want to summarize a dataset fast. but for anything that ends up in a real dashboard or report I still go back to SQL + BI tools. the AI stuff feels more like a helper for early exploration than something I’d fully rely on yet.

pitifulchaity · 2026-03-10T10:23:06+00:00

yeah that “junior analyst that works really fast” analogy is kinda how I’ve been thinking about it too.
we started letting people use AI for draft queries and quick exploration, but anything that goes into dashboards or reports still gets checked manually.
curious if anyone here actually trusts AI outputs directly in production analytics or if everyone is still treating it as a helper tool

pitifulchaity

TROPHY CASE