Databricks vs. Snowflake: How We Choose for Enterprise Clients

"Should we use Databricks or Snowflake?"

It's one of the most common questions we hear from engineering leaders evaluating their data stack. The answer — as with most architecture decisions — is "it depends." But it depends on specific, identifiable factors that we can reason through together.

This post shares how we approach the decision at Stabintel, based on real enterprise engagements.

The short version

When we recommend Databricks

We lean toward Databricks when the client has:

• Active ML/data science workloads alongside their analytics. If data scientists are building models, not just querying data, Databricks' notebook environment and MLflow integration are significantly better than Snowflake's ML offerings.

• Large-scale data engineering with complex transformations. Spark's distributed compute engine handles petabyte-scale ETL more naturally than Snowflake's SQL-based approach.

• A preference for open data formats. Delta Lake keeps your data in open Parquet files on your own cloud storage. You're never locked into a proprietary format — you can query the same data with Spark, Presto, Trino, or any tool that reads Parquet.

• Multi-cloud requirements. Databricks runs natively on AWS, Azure, and GCP with consistent APIs across all three. If your organization operates across clouds, this matters.

When we recommend Snowflake

We lean toward Snowflake when:

• SQL is the primary interface. Snowflake's SQL engine is exceptionally fast, well-optimized, and supports semi-structured data (JSON, Avro, Parquet) natively. If most of your team writes SQL — not Python or Scala — Snowflake is more natural.

• You want simplicity. No clusters to manage, no Spark tuning, no notebook infrastructure. Virtual warehouses spin up, run queries, and suspend automatically. The operational burden is significantly lower.

• Data sharing is a priority. Snowflake's data sharing and marketplace features are industry-leading. If you need to share live data with partners, customers, or other business units without copying it, Snowflake is hard to beat.

• Your workload is primarily BI and reporting. Power BI, Tableau, Looker, and other BI tools integrate deeply with Snowflake. The performance for interactive dashboards is excellent out of the box.

When we recommend both

This is more common than people expect.

A typical hybrid architecture:

1. Raw data lands in cloud storage (S3, ADLS, GCS)
2. Databricks processes and transforms the data using Spark + Delta Lake
3. Clean, modeled data is written to Snowflake for analyst consumption
4. BI tools (Power BI, Tableau) connect to Snowflake for dashboards
5. Data scientists use Databricks notebooks for ML on the same underlying data

This gives each team the tool that fits their workflow without forcing a one-size-fits-all decision.

The factors that actually matter

Beyond features, these are the practical considerations that drive the real decision:

Team skills. If your data team is Python-heavy and comfortable with notebooks, Databricks will feel natural. If they're SQL-heavy and BI-focused, Snowflake wins. Don't force a tool change on a productive team without a compelling reason.

Existing cloud contracts. Databricks on Azure has deep integration (Azure Databricks is a first-party service). Snowflake runs on all three clouds but as a third-party service. Check your existing cloud commitments and discount tiers.

Cost at scale. Both platforms can get expensive at scale. Databricks costs are driven by cluster uptime (running Spark clusters costs money even when queries aren't executing). Snowflake costs are driven by query compute (warehouses can auto-suspend). Model both before committing.

Vendor lock-in tolerance. Databricks' open-format approach (Delta Lake, open APIs) gives you more portability. Snowflake's proprietary storage means migrating out is harder. If lock-in risk matters to your organization, weight this accordingly.

How we approach it at Stabintel

We don't have a default recommendation. Every data platform engagement starts with understanding:

1. What does the team look like? Skills, size, workflow preferences.
2. What are the primary workloads? Analytics, ML, both?
3. What's the existing infrastructure? Cloud provider, current tools, data volume.
4. What's the budget model? CapEx vs. OpEx, committed spend, growth trajectory.
5. What does success look like in 12 months? Self-service analytics? Production ML models? Real-time dashboards?

The platform decision follows from the answers — not from a preference. Both Databricks and Snowflake are excellent. The right choice is the one that matches your specific situation.