I test data warehouses. Here’s what actually helped me sleep.

I’m Kayla. I break and fix data for a living. I also test it. If you’ve ever pushed a change and watched a sales dashboard drop to zero at 9:03 a.m., you know that cold sweat. I’ve been there, coffee in hand, Slack blowing up.

Over the last year I used four tools across Snowflake, BigQuery, and Redshift. I ran tests for dbt jobs, Informatica jobs, and a few messy Python scripts. Some tools saved me. Some… made me sigh. Here’s the real talk and real cases. (I unpack even more lessons in this extended write-up if you want the long version.)

Great Expectations + Snowflake: my steady helper

I set up Great Expectations (GE) with Snowflake and dbt in a small shop first, then later at a mid-size team. Setup took me about 40 minutes the first time. After that, new suites were fast.

If you’re curious how Snowflake fares in a high-stakes healthcare environment, there’s a detailed field story in this real-world account.

What I liked:

  • Plain checks felt clear. I wrote “no nulls,” “row count matches,” and “values in set” with simple YAML. My junior devs got it on day two.
  • Data Docs gave us a neat web page. PMs liked it. It read like a receipt: what passed, what failed.
  • It ran fine in CI. We wired it to GitHub Actions. Red X means “don’t ship.” Easy.

Before jumping into the war stories, I sometimes spin up BaseNow to eyeball a few sample rows—the quick visual check keeps me honest before the automated tests run.

Real save:

  • In March, our Snowflake “orders” table lost 2.3% of rows on Tuesdays. Odd, right? GE caught it with a weekday row-count check. Turned out a timezone shift on an upstream CSV dropped late-night rows. We fixed the loader window. No more gaps.
  • Another time, a “state” field got lowercase values. GE’s “values must be uppercase” rule flagged it. Small thing, but our Tableau filter broke. A one-line fix saved a demo.

Things that annoyed me:

  • YAML bloat. A big suite got long and noisy. I spent time cleaning names and tags.
  • On a 400M row table, “expect column values to be unique” ran slow unless I sampled. Fine for a guardrail, not for deep checks.
  • Local dev was smooth, but our team hit path bugs across Mac and Windows. I kept a “how to run” doc pinned.

Would I use it again? Yes. For teams with dbt and Snowflake, it’s a good base. Simple, clear, and cheap to run.

Datafold Data Diff: clean PR checks that saved my bacon

I used Datafold with dbt Cloud on BigQuery and Snowflake. The main magic is “Data Diff.” It compares old vs new tables on a pull request. No guesswork. It told me, “this change shifts revenue by 0.7% in CA and 0.2% in NY.” Comments showed up right on the PR.

Real save:

  • During Black Friday week, a colleague changed a join from left to inner. Datafold flagged a 12.4% drop in “orders_last_30_days” for Marketplace vendors. That would’ve ruined a forecast deck. We fixed it before merge.
  • Another time, I refactored a dbt model and forgot a union line. Datafold showed 4,381 missing rows with clear keys. I merged the fix in 10 minutes.

What I liked:

  • Setup was fast. GitHub app, a warehouse connection, and a dbt upload. About 90 minutes end to end with coffee breaks.
  • The sample vs full diff knob was handy. I used sample for quick stuff, full diff before big releases.
  • Column-level diffs were easy to read. Like a receipt but for data.

The trade-offs:

  • Cost. It’s not cheap. Worth it for teams that ship a lot. Hard to sell for tiny squads.
  • BigQuery quotas got grumpy on full diffs. I had to space jobs. Not fun mid-sprint.
  • You need stable dev data. If your dev seed is small, you can miss weird edge rows.

Would I buy again? Yes, if we have many PRs and a CFO who cares about trust. It paid for itself in one hairy week.

QuerySurge: old-school, but it nails ETL regression

I used QuerySurge in a migration from Teradata and Informatica to Snowflake. We had dozens of legacy mappings and needed to prove “old equals new.” QuerySurge let us match source vs target with row-level compare. It felt like a lab test.

Real cases:

  • We moved a “customers_dim” with SCD2 history. QuerySurge showed that 1.1% of records had wrong end dates after load. Cause? A date cast that chopped time. We fixed the mapping and re-ran. Green.
  • On a finance fact, it found tiny rounding drifts on Decimal(18,4) vs Float. We pinned types and solved it.

What I liked:

  • Source/target hooks worked with Teradata, Oracle, Snowflake, SQL Server. No drama.
  • Reusable tests saved time. I cloned a pattern across 30 tables and tweaked keys.
  • The scheduler ran overnight and sent a tidy email at 6:10 a.m. I kind of lived for those.

What wore me out:

  • The UI feels dated. Clicks on clicks. Search was meh.
  • The agent liked RAM. Our first VM felt underpowered.
  • Licenses. I had to babysit seats across teams. Admin work is not my happy place.

Who should use it? Teams with heavy ETL that need proof, like audits, or big moves from old to new stacks. Not my pick for fresh, ELT-first shops.

Soda Core/Soda Cloud: light checks, fast alerts

When I needed fast, human-friendly alerts in prod, Soda helped. I wrote checks like “row_count > 0 by 7 a.m.” and “null_rate < 0.5%” in a small YAML file. Alerts hit Slack. Clear. Loud.

Real save:

  • On a Monday, a partner API lagged. Soda pinged me at 7:12 a.m. Row count was flat. I paused the dashboards, sent a quick note, and nobody panicked. We re-ran at 8:05. All good.

Nice bits:

  • Devs liked the plain checks. Less code, more signal.
  • Anomalies worked fine for “this looks off” nudges.
  • Slack and Teams alerts were quick to set up.

Rough edges:

  • Late data caused false alarms. We added windows and quiet hours.
  • YAML again. I’m fine with it, but folks still mix spaces. Tabs are cursed.
  • For deep logic, I still wrote SQL. Which is okay, just know the limit.

I keep Soda for runtime guardrails. It’s a pager, not a lab.

My simple test playbook that I run every time

Fast list. It catches most messes.

  • Row counts. Source vs target. Also today vs last Tuesday.
  • Nulls on keys. If a key is null, stop the line.
  • Duplicates on keys. Select key, count(), having count() > 1. Old but gold.
  • Referenced keys. Does each order have a customer? Left join, find orphans.
  • Range checks. Dates in this year, amounts not negative unless refunds.
  • String shape. State is two letters. ZIP can start with 0. Don’t drop leading zeros.
  • Type drift. Decimals stay decimals. No float unless you like pain.
  • Slowly changing stuff. One open record per key, no overlaps.
  • Time zones. Hour by hour counts around DST. That 1–2 a.m. hour bites.

Quick real one:

  • On the fall DST shift, our hourly revenue doubled for “1 a.m.” I added a test that checks hour buckets and uses UTC. No more ghosts.

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Little gotchas that bit me (and may bite you)

  • CSVs drop leading zeros. I saw “01234” turn into “1234” and break joins.
  • Collation rules changed “Ä” vs “A” in a like filter. Locale matters.
  • Trim your strings. “ CA” is not “CA.” One space cost me a day once.
  • Casts hide sins. TO_NUMBER can turn “