You've decided to get your CCC data out of CCC. Good. Now you need somewhere to put it.
The three warehouses that dominate this conversation—Snowflake, BigQuery, and Databricks—all work for collision data. They'll all outperform CCC's reporting layer by two orders of magnitude. That's the easy part.
The harder part is which one costs less, runs faster on your specific workloads, and doesn't make your life miserable when your PE sponsor asks for a new report at 4 p.m. on a Friday.
Here's an honest comparison for collision-specific workloads.
The Workload Profile
Before comparing tools, it helps to name what a collision MSO's warehouse actually does:
- Volume: small by warehouse standards. A 100-shop MSO generates roughly 1–5M RO records, 10–30M estimate/labor line items, and 50–100M time-clock records per year. This is not big data.
- Query pattern: heavy aggregation (KPIs rolled up by shop, time range, carrier, tech). Lots of SELECTs with GROUP BYs across 1–3 fact tables.
- Concurrency: moderate. A handful of dashboards refreshing every few minutes plus 5–20 interactive users.
- Freshness: nightly or hourly, almost never true streaming.
- Complexity: incremental models, slowly-changing dimensions (rate sheets, labor codes, tech assignments), and occasionally machine-learning workloads (forecasting, anomaly detection).
If your workload looks like this, all three platforms will handle it. The differences show up in cost, ops overhead, and ML/AI capability.
Head-to-Head
Cost
For a collision MSO at 100 shops with nightly sync and dashboard workloads:
| Warehouse | Typical monthly spend | Cost shape |
|---|---|---|
| BigQuery | $400–$1,500 | Pay per byte scanned + storage. Predictable if you partition well. |
| Snowflake | $800–$2,500 | Pay per warehouse uptime + storage. Suspends when idle—idle discipline matters. |
| Databricks | $1,200–$3,500 | Pay per DBU. More expensive for BI workloads; shines on ML. |
These are typical ranges we see. You can be well above or below depending on discipline around partitioning (BigQuery), warehouse sizing (Snowflake), and cluster tuning (Databricks).
BigQuery is almost always the cheapest for pure analytics workloads at this data volume. If all you're doing is dashboards and SQL-defined KPIs, BigQuery will win on cost.
Performance on Aggregations
All three are fast enough. On a typical "cycle time by shop by month for last 3 years" query against 3M RO records:
- BigQuery: 1–3 seconds cold, sub-second warm (with results cache).
- Snowflake: 2–5 seconds cold on an M warehouse, sub-second warm.
- Databricks: 3–8 seconds cold on a small cluster, sub-second warm.
Any of these is a massive improvement over CCC's 10-minute reports. None is a clear winner at this volume.
BI Tool Integration
- BigQuery: native with Looker, solid with Tableau and Power BI via JDBC/ODBC. Query cache helps with repeated dashboard loads.
- Snowflake: excellent with all major BI tools. Dedicated Snowflake warehouse per BI workload is a clean pattern.
- Databricks: BI integration has improved significantly with SQL warehouses, but still feels second-priority. Extracts work; live connections can be slower than the alternatives.
If BI is your primary use case, Snowflake and BigQuery are easier. Databricks requires more tuning.
Ops Burden
- BigQuery: essentially serverless. No clusters to size, no warehouses to suspend. Partition correctly and you barely touch infrastructure.
- Snowflake: minimal ops but more dials to turn. Warehouse sizing, suspend timeouts, multi-cluster settings. Easy to spend 30% more than needed if you don't watch it.
- Databricks: real ops work. Clusters, notebooks, job orchestration, Unity Catalog, cluster policies. Most value on the ML side; more overhead than you need for pure analytics.
For an MSO without a dedicated data engineer, BigQuery wins here. For a team with data engineering chops, any of them is manageable.
ML / AI Workloads
If you plan to run anomaly detection, fraud scoring, forecasting, or intake copilots:
- Databricks: purpose-built. Notebooks, MLflow, model registry, feature store. If ML is core, Databricks wins.
- BigQuery: BigQuery ML is surprisingly capable for simple use cases (linear, logistic, tree models, forecasting via ARIMA_PLUS). Tight integration with Vertex AI for deeper work.
- Snowflake: Snowpark ML is improving fast. Not as mature as Databricks but solid for tabular ML.
If your AI workload is "forecast vehicles out for 30 days" and "flag anomalous ROs," any of the three works. If you're building custom models across collision data, Databricks's maturity shows.
What We Recommend by Stage
Single shop or small group (1–5 shops): you probably don't need a warehouse yet. Clean CCC exports into a managed Postgres or even a well-structured spreadsheet pipeline can carry you. Don't overinvest.
Growing MSO (5–25 shops): BigQuery. The cheapest path to a real analytics stack. Partition by date, cluster by shop, call it done. Minimum ops burden for a team that doesn't have dedicated data engineering.
Established MSO (25–100 shops): BigQuery or Snowflake. BigQuery if cost discipline matters and you're mostly doing BI. Snowflake if you want more BI-tool polish and have the budget. Either works.
Large MSO with ML ambitions (100+ shops, forecasting, AI copilots): Databricks or Snowflake + Snowpark. If you're running models in production, Databricks's lifecycle tools pay for themselves. If you want to keep the analytics and ML in the same tool, Snowflake is closing the gap fast.
Already in the Microsoft or AWS stack: consider Fabric or Redshift. They're not in our top three for collision workloads specifically, but if the rest of your business lives in Azure or AWS, the integration tax of a cross-cloud warehouse is real.
The Thing That Actually Matters
After several collision MSO deployments, the warehouse choice matters less than:
- Clean data modeling. A bad data model in Snowflake beats a good data model in nothing, but it loses to a good data model anywhere.
- Sync reliability. If your CCC sync drops 4% of records silently, no warehouse saves you.
- Documented definitions. If "cycle time" means three different things in three dashboards, your warehouse won't fix it.
- Ops discipline. All three warehouses can be cheap or expensive depending on how you use them.
Pick the warehouse that your team can operate well. That's a better predictor of success than the benchmarks.