Choosing a Data Platform: Snowflake vs Databricks and the Residency Question

Choosing between Snowflake and Databricks is a genuine fork for a firm maturing its data capability, and most comparisons answer the wrong question. They benchmark performance and features, which for many workloads have converged, and skip the two things that actually decide the outcome for a regulated firm: how the platform fits the work you do, and where your data is allowed to live. Get those right and either platform can succeed; get them wrong and the best benchmark in the world will not save the decision.

The real difference in fit

The honest distinction is less about capability than about centre of gravity. Databricks grew from data engineering and machine learning, and suits firms whose core need is large-scale processing, data science and AI on a lakehouse foundation. Snowflake grew from the data warehouse, and suits firms whose core need is analytics, sharing and SQL-first access with minimal operational overhead. Both have moved onto each other’s territory — Snowflake does more data science, Databricks does more warehousing — so the question is which one matches how your teams actually work and what your dominant workload really is, not which has the longer feature list.

The residency question most comparisons skip

For a regulated firm, the decisive question is often not performance but sovereignty: where does the data physically sit, and can you prove it to a supervisor? Both platforms run on the major clouds and offer specific regions, but the details matter. Where are the compute and storage located for your workloads, where do backups and replicas go, and where does metadata and telemetry flow? A platform that keeps your primary data in-region but replicates or processes elsewhere can quietly put you offside a residency requirement under GDPR, DORA, a GCC PDPL or a sovereignty mandate. This has to be asked explicitly and answered contractually, early — an assumption is not a control.

How to actually run the choice

• Define the dominant workload first. Engineering-and-ML-heavy points toward one centre of gravity; analytics-and-sharing-heavy toward the other. Decide what you are actually optimising for.
• Fix the residency requirements as non-negotiable. Map your data-location obligations before you evaluate, and treat them as pass/fail criteria, not tie-breakers.
• Cost the decade, not the proof-of-concept. Both platforms’ real cost emerges at scale, with the consumption model and the operational overhead; a cheap pilot can hide an expensive steady state.
• Evaluate against your requirements, not the vendor’s demo. The platform matters less than a selection run on your workload and your compliance constraints, ideally by someone with no stake in which vendor wins.

Snowflake and Databricks are both strong platforms, which is exactly why the decision should turn on fit and residency rather than a feature bake-off. For a regulated firm, the data-location question is the one that will still matter in three years, long after the benchmark numbers are forgotten.

Who this is for

This reading is for:

• CTOs and heads of data choosing a platform for a maturing data estate
• Regulated firms where data location is a compliance question, not a preference
• Boards approving a decision that will anchor the data strategy for years
• Data leaders tired of vendor comparisons that ignore sovereignty

Sixteen Pillars runs the data-platform selection against your dominant workload and your data-residency obligations, with no stake in which vendor wins. Pricing is published at /pricing/. If this is live for your organisation and you would like an independent reading, the place to start is a conversation.

Sixteen Pillars is a technology governance consultancy based in Cyprus. Engagements run remote across the EU, UK, and Middle East, with on-site time where the engagement requires it.