Applies toDatabricks (any cloud), Snowflake, Microsoft Fabric, cloud warehouses such as Amazon Redshift, Google BigQuery and Azure Synapse
Start from your situation
| Question | Why it matters |
|---|---|
| Main workloads: BI/SQL, data engineering, streaming, ML/AI? | Platforms have different strengths per workload |
| Team skills: SQL-first analysts, Spark/Python engineers, Power BI developers? | Productivity and support load |
| Existing estate: Microsoft 365/Power BI, AWS, GCP, Oracle? | Integration, identity, networking, procurement |
| Data volumes and concurrency? | Cost model and scaling behaviour |
| Governance and residency needs? | Catalog, lineage, region availability |
| Openness: must data stay in open formats you control? | Lock-in and multi-engine access |
How they tend to compare
| Databricks | Snowflake | Microsoft Fabric | Cloud-native warehouses | |
|---|---|---|---|---|
| Centre of gravity | Lakehouse, Spark, ML/AI, streaming | SQL warehouse, sharing, ease of operation | SaaS analytics suite around OneLake and Power BI | SQL warehousing within one cloud |
| Storage | Open tables (Delta/Iceberg) in your storage | Managed storage (plus Iceberg tables) | OneLake (Delta), shortcuts to other storage | Managed (plus lake integration) |
| Compute model | Clusters/serverless, DBUs | Warehouses, credits | Shared capacity (CU) across workloads | Provisioned or serverless |
| Governance | Unity Catalog | Horizon / roles | Purview integration, OneLake security | Cloud-native IAM + catalog |
| Typical strength | Engineering-heavy, ML, large-scale processing | SQL analytics with low ops, data sharing | Microsoft-centric orgs, BI-heavy | Single-cloud shops with SQL focus |
These are tendencies. All of them overlap more every year, so verify current capabilities for your use cases.
Run a proof of value
- Pick 3–5 representative workloads (a heavy transformation, a BI dashboard with concurrency, an ingestion stream, an ML/AI job if relevant).
- Use your data (or a realistic copy) and your team.
- Measure: time to build, run time, concurrency behaviour, cost for a month of projected usage, operational effort (monitoring, access control, CI/CD).
- Score against weighted criteria agreed before the test.
Questions that often decide it
- Who will operate it, and what skills do they already have?
- How does cost behave at your concurrency (BI peaks) and for always-on workloads?
- How does identity integrate with your IdP, and how are permissions governed?
- Can other engines read your data without copying it (open formats)?
- What's the network and private connectivity story in your cloud?
- What's the exit path if you change direction in five years?
Coexistence is normal
Common patterns:
- Databricks for engineering and ML, Power BI/Fabric for BI on top of the same Delta tables.
- Snowflake as the warehouse, with Spark or Databricks for heavy processing in the lake.
- Fabric for departmental analytics, with an enterprise lakehouse elsewhere, connected via shortcuts or sharing.
Design for shared open storage and a clear catalog strategy, so coexistence doesn't mean copies everywhere.
Tip
Weight operating effort and team fit as heavily as features. The platform your team can run well usually beats the one with the best benchmark.
Checklist
- Workloads, skills, estate and governance needs documented
- Criteria weighted before the evaluation
- Proof of value with real data and team
- Cost projected at real concurrency
- Openness and exit path considered
- Coexistence and catalog strategy decided