Snowflake vs Databricks: Which Data Platform?

Snowflake vs Databricks

Snowflake and Databricks are the two most prominent cloud data platforms, and they approach the market from opposite directions. Snowflake began as a cloud data warehouse: SQL first, fully managed, with storage and compute separated and almost no tuning required. Databricks began as a managed Spark platform for data engineering and machine learning, then built the lakehouse around Delta Lake and open formats. Each has spent years adding the other's strengths — Snowflake now runs Python, notebooks, ML, and Iceberg tables; Databricks now offers serverless SQL warehouses and BI-grade performance — so the overlap is large.

Short verdict: choose Snowflake when SQL analytics and BI are the center of gravity and you value simplicity, governance, and minimal operations. Choose Databricks when large-scale data engineering, streaming, and machine learning on open formats are central, and your team is comfortable with Spark and Python. Both run on all major clouds, both support Iceberg, and both can be the right answer; team skills and dominant workloads usually decide.

TL;DR

Snowflake: SQL-first managed warehouse; virtual warehouses billed in credits; Time Travel, zero-copy cloning, data sharing; Snowpark for Python.
Databricks: lakehouse on Spark and Delta Lake; notebooks, jobs, streaming, MLflow, model serving; serverless SQL warehouses billed in DBUs.
Openness: Databricks stores data in open Delta or Iceberg tables in your cloud storage; Snowflake uses managed storage by default and supports Iceberg tables.
Governance: Snowflake Horizon and Databricks Unity Catalog both offer access control, lineage, and discovery.
AI: both offer LLM functions, vector search, and agent tooling — Snowflake Cortex and Databricks Mosaic AI.
Cost depends far more on workload design and controls than list prices; both can be economical or very expensive.

Quick Example

The same incrementally maintained daily revenue table. Snowflake, with a dynamic table that refreshes within an hour of source changes:

Databricks SQL, with a materialized view refreshed on a schedule:

For straightforward SQL transformations, the platforms look nearly identical. The differences appear in data engineering at scale, ML workflows, storage openness, and how compute is provisioned and billed.

Core Concepts

Head-to-Head

Snowflake's Model

Data lives in Snowflake-managed micro-partitions with automatic clustering and statistics. Virtual warehouses are independent compute clusters that start in seconds, scale up or out for concurrency, and suspend automatically. Features such as Time Travel, zero-copy cloning for development environments, and cross-account data sharing are distinctive strengths. Snowpark brings DataFrame APIs and Python functions into Snowflake's engine. See Snowflake.

Databricks' Model

Data lives in open-format tables in your own object storage, governed by Unity Catalog. Compute runs as Spark clusters, serverless jobs, or SQL warehouses using the Photon engine. Notebooks, job orchestration, declarative pipelines, MLflow experiment tracking, and model serving make it a full environment for engineers and data scientists. See Spark and Data Lakehouse.

Openness and Lock-In

Databricks' tables are open formats in your storage, readable by other engines. Snowflake's native tables are readable only through Snowflake, but Snowflake-managed Iceberg tables and external catalogs give similar openness where needed. In both, proprietary features — pipelines, AI functions, governance metadata — create lock-in that matters more than the file format. See Apache Iceberg.

Cost and Operations

Teams on both platforms most often overspend through idle or oversized compute, inefficient transformations rerun too frequently, and unmonitored ad hoc queries.

The Decision Framework

Best Practices

Decide by Dominant Workload

List your top workloads by cost and importance — BI queries, ELT, streaming, ML training — and evaluate both platforms on those, not on feature checklists.

Run a Proof of Concept With Real Data

Benchmark representative queries and pipelines, including concurrency and cost, for a few weeks. Vendor benchmarks rarely match your workload.

Put Cost Controls in Place on Day One

Auto-suspend and auto-terminate, budgets, tagging, and usage dashboards prevent surprise invoices on either platform.

Keep Transformations Portable

Tools such as dbt and open table formats keep business logic and data movable if your needs change.

Common Mistakes

Choosing From Marketing Categories

Leaving Compute Running

Running Both Without a Plan

FAQ

Is Snowflake or Databricks better?

Neither universally. Snowflake is generally simpler for SQL analytics and BI with minimal operations. Databricks is generally stronger for large-scale data engineering, streaming, and machine learning on open formats. Both have converged significantly.

Is Databricks a data warehouse?

Databricks offers SQL warehouses and positions its platform as a lakehouse that includes warehouse capabilities. It can serve BI workloads directly from Delta or Iceberg tables.

Can Snowflake use open table formats?

Yes. Snowflake supports Apache Iceberg tables stored in your own cloud storage, managed by Snowflake or an external catalog, so other engines can read the same data.

Which is cheaper?

It depends on workloads and discipline. Both bill for compute usage, and costs are driven mostly by warehouse or cluster sizing, idle time, and pipeline efficiency. A proof of concept with real workloads is the only reliable comparison.

Can I use dbt with both?

Yes. dbt has mature adapters for Snowflake and Databricks, and many teams use dbt to keep transformation logic consistent and portable across platforms.