I’m Kayla. I lead data work at a mid-size health system. I moved our old warehouse from a slow on-prem server to Snowflake. I used it every day for 18 months. I built the models. I fixed the jobs when they broke at 3 a.m. I also drank a lot of coffee. You know what? It was worth it—mostly.
What we built (in plain talk)
We needed one place for truth. One spot where Epic data, lab feeds, claims, and even staffing data could live and play nice.
- Core: Snowflake (our warehouse in the cloud)
- Sources: Epic Clarity and Caboodle (SQL Server), Cerner lab, 837/835 claims, Kronos, Workday
- Pipes and models: Matillion for loads, dbt for models, a few Python jobs, Mirth Connect for HL7
- Reports: Tableau and a little Power BI
If you’re starting from scratch and want to see what a HIPAA-ready architecture looks like, Snowflake’s own HIPAA Data Warehouse Built for the Cloud guide walks through the essentials.
We set up daily loads at 4 a.m. We ran near real-time feeds for ED arrivals and sepsis alerts. We used a patient match tool (Verato) to link records. Simple idea, hard work.
The first week felt fast—and a bit wild
Snowflake was quick to start. I spun up a “warehouse” (their word for compute) in minutes. We cloned dev from prod with no extra space. That was cool. Time Travel saved me on day 6 when a junior analyst ran a bad delete. We rolled back in minutes. No drama.
But I hit a wall on roles and rights. PHI needs care. I spent two long nights sorting who could see what. We set row rules by service line. We masked birth dates to month and year. It took trial and error, and a few “whoops, that query failed” moments, but it held.
Real wins you can feel on the floor
- Sepsis: Our ICU sepsis dashboard refreshed every 15 minutes. We cut time to first antibiotic by 14 minutes on average. That sounds small. It isn’t. It saved stays.
- Readmits: For heart failure, 30-day readmits dropped by 3.2 points in six months. We found “frequent flyer” patterns, called them early, and set follow-ups before discharge.
- ED flow: We tracked “door to doc” and “left without being seen” in near real time. Weekend waits dropped by about 12 minutes. That felt human.
- Supply chain: During flu season, PPE burn rate showed we were over-ordering. We canceled two rush orders and saved about $220k. My buyer hugged me in the hall. Awkward, but sweet.
- Revenue: We flagged claims with likely denials (we saw bad combos in the codes). Fixes at the front desk helped. We cut avoidable write-offs by about $380k in a quarter.
What Snowflake did best for us
- Speed on big joins: Our “all visits for the last 2 years” query went from 28 minutes on the old box to 3 minutes on a Medium warehouse.
- Easy scale: Month-end? Bump to Large, finish fast, and drop back. Auto-suspend keeps costs in check.
- Zero-copy clones: Perfect for testing break-fix without fear.
- Data sharing: We gave a payer a clean view with masked fields. No file toss. No FTP pain.
- Time Travel: Saved me twice from bad deletes. Fridays, of course.
What hurt (and how we patched it)
- FHIR is not “push button”: We had to build our own views and map HL7 FHIR. Mirth helped, but we still wrote a lot of code.
- Epic upgrades break stuff: When Epic changed a column name, our nightly job cried. We added dbt tests, schema checks, and a “red light” channel. Still, a few 3 a.m. pings.
- Identity match is messy: Twins, hyphenated names, address changes. Our duplicate rate was 1.8%. After tuning the match tool and rules, we got it near 0.4%. Close enough to trust, not perfect.
- Costs can spike: One Tableau workbook ran a cross-join and woke a Large warehouse. Ouch. We set resource monitors and query limits. We also taught folks to use extracts.
- SCD history needs care: We built Type 2 in dbt with macros. It works. But Snowflake doesn’t hand that to you out of the box.
Costs in plain words
We’re mid-size. Storage was cheap. Compute was the big line.
- Normal month: about $38k total (compute + storage).
- Month-end with big crunch: up to $52k.
- We set auto-suspend to 60 sec and used Small/Medium most days. That cut spend by ~22%.
- Cross-region shares add fees. Keep data close to users when you can.
Is it worth it? For us, yes. The readmit work alone paid for it.
If you’d like the detailed cost-control checklist I use (scripts, monitors, and all), I’ve posted it on BaseNow so you can borrow what you need. I also pulled together a step-by-step recap of the entire migration—what went right, what broke, and the coffee count. You can find that case study if you want the full story.
Security and HIPAA stuff (yes, I care)
- We signed the BAA. Data was encrypted at rest and in flight.
- Row rules and masking kept PHI safe. We hid names for wide reports and showed only what each team needed.
- Audits were fine. We logged who saw what. Our infosec lead slept better. Honestly, so did I.
- Snowflake’s recent HITRUST r2 certification also checked a big box for our auditors.
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Daily life with the stack
Most days were smooth. Jobs kicked off at 4 a.m. I checked the health page, sipped coffee, and fixed the one thing that squeaked. On cutover weekend, we camped near the command center with pizza. Not cute, but it worked.
When flu picked up, we bumped compute from Small to Medium for the morning rush, then back down by lunch. That rhythm kept people happy and bills sane.
Who should pick this
- Good fit: Hospitals with 200+ beds, ACOs, health plans, labs with many feeds, groups with Epic or Cerner and a real need for near real-time views.
- Might be heavy: A single clinic with one EMR and a few static reports. You may not need this muscle yet.
Still on the fence about warehouse versus lake—or even a full-on mesh? I put each approach through its paces and wrote up the real pros and cons in this deep dive.
What I’d change
- A simple, native FHIR toolkit. Less glue. Fewer scripts.
- Easier role setup with PHI presets. A “starter pack” for healthcare would help.
- Cheaper cross-region shares. Or at least clearer costs up front.
Little moments that stuck with me
- A Friday night delete fixed by Time Travel in five minutes. I still smile about that save.
- A charge nurse told me the sepsis page “felt like a head start.” That line stays with me.
- A CFO who hated “data chat” stopped by and said, “That denial chart? Keep that one.” I printed it. Kidding. Kind of.
My verdict
Snowflake as our healthcare warehouse scored an 8.5/10 for me. It’s fast, flexible, and strong on sharing. You must watch costs, plan for schema churn, and build your own FHIR layer. If you’re ready for that, it delivers.
Would I use it again for a hospital? Yes. With guardrails on cost, clear tests on loads, and a friendly channel for “Hey, this broke,” it sings. And when flu season hits, you’ll be glad it can stretch.