Hi, I’m Kayla. I’ve run data at a scrappy startup and at a big retail brand. I’ve used Snowflake, BigQuery, and Redshift. I break things. I fix them. And I take notes.
If you’d like the longer, unfiltered story behind these messy wins, check out my full write-up on BaseNow: My Go-To Rules for a Data Warehouse (Told From My Own Messy Wins).
This is my review of what actually works for me when I build and care for a data warehouse. It’s not a lecture. It’s field notes with real bumps, a few “oh no” moments, and some sweet saves. If you're after a more structured checklist of warehouse best practices geared toward growing teams, the Integrate.io crew has a solid rundown right here.
Start Simple, Name Things Well
I used to get cute with names. Bad idea. Now I keep it boring:
- stg_ for raw, cleaned data (stg_orders)
- int_ for mid steps (int_orders_with_discounts)
- dim_ for lookup tables (dim_customer)
- fact_ for event tables (fact_orders)
Want to see how the very first data model I ever built shook out—warts and all? Here’s the play-by-play: I Built a Data Warehouse Data Model—Here’s What Actually Happened.
In 2022, a Looker explore kept breaking because one teammate named a table orders_final_final. Funny name, sure. But it cost us two hours on a Monday. After we switched to the simple tags above, QA got faster. Fewer “where did that table go?” moments, too. For a formal deep dive on why disciplined, consistent prefixes matter (and what good ones look like), this naming-convention cheat sheet is gold: Data Warehouse Naming Conventions.
Star Shape Wins Most Days
When in doubt, I lean on a star model. One big fact. Little helpful dims. It’s not fancy. It just works.
Real example from our e-com shop:
- fact_orders with one row per order
- dim_customer with Type 2 history (so we keep old addresses and names)
- dim_product (SKU, brand, category)
- dim_date (day, week, month)
Curious how this stacks up against other modeling patterns I’ve tried? Here’s my candid comparison: I Tried Different Data Warehouse Models—Here’s My Take.
Before this, we had a chain of joins that looked like spaghetti. Dashboards took 40+ seconds. After the star setup, the main sales board ran in 5–8 seconds. Not magic. Just less chaos.
Partitions and Clusters Save Real Money
I learned this the hard way on BigQuery. A summer intern ran a full-table scan on a year of web logs. Boom—30 TB read. The cost alert hit my phone while I was in line for tacos.
If you’ve got interns or younger analysts itching to poke around, carving out a supervised sandbox chat where they can sanity-check queries first can save serious cash. One quick, no-sign-up option is InstantChat’s teen-friendly room — it’s a moderated space where new folks can fire off newbie questions in real time without cluttering your main engineering channels.
We fixed it:
- Partition by event_date
- Cluster by user_id and path
Next run: 200 GB. That’s still big, but not scary. Same move in Snowflake? I use date-based micro-partitions and sort keys where it helps. On Redshift, I set sort keys and run VACUUM on a schedule. Not cute, but it keeps things fast.
Fresh Beats Perfect (But Test Everything)
I run ELT on a schedule and sort by impact. Finance needs early numbers? Hourly. Product? Maybe every 3 hours. Ads spend? Near real time during promos.
The trick: tests. I use dbt for this:
- unique and not_null on primary keys
- relationships (orders.customer_id must exist in dim_customer)
- freshness checks
For the full breakdown of the testing playbook that’s saved my bacon more than once, see: I Tried a Data Warehouse Testing Strategy—Here’s What Actually Worked.
These tests saved me during Black Friday prep. A dbt test flagged a 13% drop in orders in staging. It wasn’t us. It was a checkout bug. We caught it before the rush. The team brought me donuts that week. Nice touch.
CDC When You Need It, Not When You Don’t
We used Debezium + Kafka for Postgres change data. Then Snowpipe pushed it into Snowflake. It felt heavy at first. But support chats and refunds need near real time. So yes, it earned its keep for that stream.
Not sure when to keep data in an operational data store versus a warehouse? Here’s my field guide: ODS vs. Data Warehouse—How I’ve Used Both and When Each One Shines.
But for Salesforce? We used Fivetran. I tried to build it myself once. Look, it worked, but it took way too much time to keep it alive. I’d rather write models than babysit API limits all day.
Clear SLAs Beat Vague Wishes
“Real time” sounds great. It also costs real money. I set simple rules with teams:
- Finance: hourly until noon; daily after
- Marketing: 15 minutes during promos; 1 hour off-peak
- Product: daily is fine; hourly on launch days
We put these in Notion. People stopped asking “Is the data fresh?” They could check the SLA. And yes, we allow “break glass” runs. But we also measure the blast.
If you’re wrangling an enterprise-scale warehouse and want to know what actually works in the real world, you’ll like this deep dive: What Actually Works for an Enterprise Data Warehouse—My Hands-On Review.
Row-Level Security Keeps Me Sane
One time, an intern ran a query and saw salary data. I felt sick. We fixed it by Monday:
- Use roles for each group (sales_analyst, finance_analyst)
- Use row-level filters (BigQuery authorized views; Snowflake row access policies)
- Keep write access tight. Like, tight tight.
Now sales sees only their region. Finance sees totals. HR sees sensitive stuff. And I sleep better.
If you’re wondering how tight testing and alerting can translate into better sleep, here’s a story you’ll appreciate: I Test Data Warehouses—Here’s What Actually Helped Me Sleep.
Docs and Lineage: The Boring Hero
I keep docs in two places:
- dbt docs for models and lineage
- One-page team guide in Notion: table naming, keys, and joins you should never do
For an even deeper bench of templates and examples, the free resources at BaseNow have become one of my secret weapons for leveling up documentation without adding extra toil.
When someone new asks, “Where do I find churn by cohort?” I show the doc. If I get the same question three times, I write a tiny guide with a screenshot. It takes 10 minutes. It saves hours later.
Cost Controls That Help (Not Hurt)
Real things that worked:
- Snowflake: auto-suspend after 5 minutes; auto-resume on query
- BigQuery: per-project quotas and cost alerts at 50%, 80%, 100%
- Redshift: right-size nodes; use concurrency scaling only for peaks
We saved 18% in one quarter by shutting down a weekend dev warehouse that no one used. It wasn’t clever. It was just a toggle we forgot to flip.
Backups, Clones, and “Oops” Moments
I once dropped a table used by a morning dashboard. My phone blew up. We fixed it fast with a Snowflake zero-copy clone. We also used time travel to pull data from 3 hours earlier. Five minutes of panic. Then calm. After that, I set daily clone jobs and tested them. Not just on paper—tested for real.
If you’re curious how Snowflake holds up in a high-stakes environment, here’s what happened when I ran a hospital’s warehouse on it: I Ran Our Hospital’s Data Warehouse on Snowflake—Here’s How It Really Went.
Analysts Need Joy, Too
I add small things that reduce pain:
- Surrogate keys as integers (less join pain)
- A shared dim_date with holidays and