Note: This is a fictional first-person review written for learning.
I spend a lot of time with warehouses. Not the kind with forklifts. The data kind. I build models, fix weird joins, and help teams get reports that don’t lie. Some models felt smooth. Some made me want to take a long walk.
If you’d like a deeper, step-by-step breakdown of the experiments I ran, check out this expanded write-up on trying different data-warehouse models.
Here’s what stood out for me, model by model, with real-life style examples and plain talk. I’ll keep it simple. But I won’t sugarcoat it.
The Star That Actually Shines (Kimball)
This one is the crowd favorite. You’ve got a fact table in the middle (numbers), and dimension tables around it (things, like product or store). It’s easy to see and easy to query.
For a crisp refresher on how star schemas power OLAP cubes, the Kimball Group’s short guide on the pattern is worth a skim: Star Schema & OLAP Cube Basics.
- Where I used it: a retail group with Snowflake, dbt, and Tableau.
- Simple setup: a Sales fact with date_key, product_key, store_key. Then Product, Store, and Date as dimensions.
- We added SCD Type 2 on Product. That means we kept history when a product changed names or size.
What I liked:
- Dashboards felt fast. Most ran in 2–5 seconds.
- Analysts could write SQL with less help. Fewer joins. Fewer tears.
- Conformed dimensions made cross-team work sane. One Product table. One Customer table. Life gets better.
What bugged me:
- When the source changed a lot, we rebuilt more than I wanted.
- Many-to-many links (like customer to household) needed bridge tables. They worked, but it felt clunky.
- Wide facts got heavy. We had to be strict on grain (one row per sale, no sneaky extras).
Little trick that helped:
- In BigQuery, we clustered by date and customer_id. Scan size dropped a ton. Bills went down. Smiles went up.
Snowflake Schema (Not the Company, the Shape)
This is like star, but dimensions are split out into smaller lookup tables. Product splits into Product, Brand, Category, etc.
- Where I used it: marketing analytics on BigQuery and Looker.
What I liked:
- Better data hygiene. Less copy-paste of attributes.
- Great for governance folks. They slept well.
What bugged me:
- More joins. More cost. More chances to mess up filters.
- Analysts hated the hop-hop-hop between tables.
Would I use it again?
- Only when reuse and control beat speed and ease. Else, I stick with a clean star.
Inmon 3NF Warehouse (The Library)
This model is neat and very normalized. It feels like a library with perfect shelves. It’s great for master data and long-term truth.
- Where I used it: a healthcare group on SQL Server and SSIS, with Power BI on top.
What I liked:
- Stable core. Doctors, patients, visits—very clear. Auditors were happy.
- Changes in source systems didn’t break the world.
What bugged me:
- Reports were slow to build right on top. We still made star marts for speed.
- More tables. More joins. More time.
My note:
- If you need a clean “system of record,” this is strong. But plan a mart layer for BI.
If you’re curious how a heavily regulated environment like healthcare handles modern cloud warehouses, my colleague’s narrative about moving a hospital’s data warehouse to Snowflake is worth a read: here’s how it really went.
Data Vault 2.0 (The History Buff)
Hubs, Links, and Satellites. It sounds fancy, but it’s a simple idea: store keys, connect them, and keep all history. It’s great when sources change. It’s also great when you want to show how data changed over time.
For a deeper dive into the principles behind this approach, the IRI knowledge base has a solid explainer on Data Vault 2.0 fundamentals.
- Where I used it: Azure Synapse, ADF, and dbt. Hubs for Customer and Policy. Links for Customer-Policy. Satellites for history.
- We then built star marts on top for reports.
What I liked:
- Fast loads. We added new fields without drama.
- Auditable. I could show where a value came from and when it changed.
What bugged me:
- Storage got big. Satellites love to eat.
- It’s not report-ready. You still build a star on top, so it’s two layers.
- Training was needed. New folks got lost in hubs and links.
When I pick it:
- Many sources, lots of change, strict audit needs. Or you want a long-term core you can trust.
Lakehouse with Delta (Raw, Then Clean, Then Gold)
This is lakes and warehouses working together. Files first, tables later. Think Bronze (raw), Silver (clean), Gold (curated). Databricks made this feel smooth for me.
- Where I used it: event data and ads logs, with Databricks, Delta Lake, and Power BI.
- Auto Loader pulled events. Delta handled schema drift. We used Z-Ordering on big tables to speed lookups.
What I liked:
- Semi-structured data was easy. JSON? Bring it.
- Streaming and batch in one place. One pipeline, many uses.
- ML teams were happy; BI teams were okay.
What bugged me:
- You need clear rules. Without them, the lake turns to soup.
- SQL folks sometimes missed a classic warehouse feel.
When it shines:
- Clickstream, IoT, logs, fast feeds. Then build tidy gold tables for BI.
Side note:
- I’ve also used Iceberg on Snowflake and Hudi on EMR. Same vibe. Pick the one your team can support.
One Big Table (The Sledgehammer)
Sometimes you make one huge table with all fields. It can be fast. It can also be a pain.
- Where I used it: finance month-end in BigQuery. One denormalized table with every metric per month.
What I liked:
- Dashboards flew. Lookups were simple.
- Less room for join bugs.
What bugged me:
- ETL was heavy. Any change touched a lot.
- Data quality checks were harder to aim.
I use this for narrow, stable use cases. Not for broad analytics.
A Quick Word on Data Mesh
This is not a model. It’s a way to work. Teams own their data products. I’ve seen it help large groups move faster. But it needs shared rules, shared tools, and strong stewardship. Without that, it gets noisy. Then your warehouse cries.
For a fuller comparison of Data Lake, Data Mesh, and Data Fabric in the real world, take a look at my candid breakdown.
What I Reach For First
Short version:
- Need fast BI with clear facts? Star schema.
- Need audit and change-proof ingestion? Data Vault core, star on top.
- Need a clean system of record for many systems? Inmon core, marts for BI.
- Got heavy logs and semi-structured stuff? Lakehouse with a gold layer.
- Need a quick report for one team? One big table, with care.
My usual stack:
- Raw zone: Delta or Iceberg. Keep history.
- Core: Vault or 3NF, based on needs.
- Marts: Kimball stars.
- Tools: dbt for models, Airflow or ADF for jobs, and Snowflake/BigQuery/Databricks. Power BI or Tableau for viz.
Real Moments That Taught Me Stuff
- SCD Type 2 saved us when a brand reorg hit. Old reports kept old names. New reports showed new names. No fights in standup.
- We forgot to cluster a BigQuery table by date. A daily report scanned way too much. Bills went up. We fixed clustering, and the scan dropped by more than half.
- A vault model let us add a new source system in a week. The mart took another week. Still worth it.
- A lakehouse job choked on bad JSON. Auto Loader with schema hints fixed it. Simple, but it felt like magic.
Pitfalls I Try to Avoid
- Mixing grains in a fact table. Pick one grain. Tattoo it on the README.
- Hiding business logic in ten places. Put rules in one layer, and say it out loud.
- Over-normalizing a star. Don’t turn stars into snowstorms.
- Skipping data tests. I use dbt tests for keys, nulls, and ranges. Boring, but it saves weekends.
My Bottom Line
There’s no one model to rule them all. I know, I wish. But here’s the thing: the right model matches the job, the team, and the data. Stars make