Databricks vs Snowflake: A Practitioner's Comparison

I recently completed a platform comparison between Databricks and Snowflake, working hands-on with both for a client's data infrastructure decision. Here's what I learned - beyond the marketing materials.

Different Origins, Converging Features

Snowflake started as a cloud-native data warehouse. Its DNA is SQL-first analytics with a beautiful separation of storage and compute. It does one thing exceptionally well: let analysts query data fast.

Databricks started as a commercial wrapper around Apache Spark. Its DNA is big data processing, data science workloads, and handling unstructured data. It expanded into SQL analytics but came from the engineering side.

Both platforms have converged significantly. Snowflake added Snowpark for Python/Scala processing. Databricks improved its SQL interface with Databricks SQL. But the underlying philosophies still show.

When Snowflake Wins

Your workload is primarily SQL analytics. If 80%+ of your work is analysts running queries and building dashboards, Snowflake's experience is hard to beat. The query optimizer is excellent, the UI is intuitive, and the learning curve is gentle.

You want simplicity. Snowflake is famously easy to manage. Credit-based pricing is straightforward. You don't need to think about clusters, node types, or Spark configurations.

Your team is SQL-heavy. Analysts and analytics engineers who live in SQL will be productive immediately. dbt + Snowflake is a proven, well-documented combination.

You need instant scaling. Snowflake's warehouse scaling is genuinely impressive. Spin up compute in seconds, scale to handle massive query loads, then scale back down.

When Databricks Wins

You have significant data engineering workloads. If you're building complex ETL pipelines, processing streaming data, or working with data at massive scale, Databricks' Spark foundation shines.

You need ML/AI capabilities. MLflow is native to Databricks. Training models, experiment tracking, model serving - it's all integrated. Snowflake has ML features, but Databricks was built for this.

You work with unstructured data. Images, text, JSON blobs, log files - Spark handles these naturally. The lakehouse architecture (Delta Lake) lets you query structured and unstructured data together.

You want an open format. Delta Lake uses Parquet files. Your data isn't locked into a proprietary format. This matters for some organizations more than others.

You have a strong engineering team. Databricks offers more power and flexibility - but requires more expertise to use well.

The Real Differences I Found

Learning curve. Snowflake: my client's analysts were productive in days. Databricks: the learning curve is steeper, especially for non-engineers. Notebooks, clusters, and Spark concepts take time.

Cost model. Snowflake's credit system is easier to understand and predict. Databricks' DBU pricing with different SKUs for different workload types is more complex. Both can get expensive at scale - but in different ways.

SQL performance. For standard analytics queries, both are fast. Snowflake felt slightly snappier for ad-hoc queries. Databricks SQL has improved dramatically but still shows its Spark origins.

Data engineering. Databricks is clearly stronger here. Building complex pipelines with Python, orchestrating multi-step workflows, handling schema evolution - Databricks feels native. In Snowflake, you're often reaching for external tools.

Governance. Unity Catalog (Databricks) and Snowflake's governance features are both maturing. Neither is perfect. Unity Catalog is more comprehensive but newer.

The Cost Question

Everyone wants to know: which is cheaper?

The honest answer: it depends entirely on your workload.

Snowflake tends to be more predictable. You pay for compute time and storage. Easy to model.

Databricks can be cheaper for heavy compute workloads if you optimize cluster configurations. It can also be more expensive if you don't.

I've seen organizations where Snowflake was 2x cheaper. I've seen organizations where Databricks was 2x cheaper. Usage patterns matter more than list prices.

My Recommendations

Choose Snowflake if: - Your primary users are analysts and analytics engineers - SQL is your team's strongest skill - You value simplicity over flexibility - Your workload is 80%+ analytics queries

Choose Databricks if: - You have significant data engineering needs - ML/AI is a major part of your strategy - You work with diverse data types (structured, semi-structured, unstructured) - You have engineering resources to optimize the platform

Consider both if: - You have distinct teams with different needs - Some organizations run Snowflake for BI/analytics and Databricks for data science - The cost of running two platforms may be worth the specialization

The Convergence Reality

Both platforms are rapidly adding features to compete with each other. Snowflake's Snowpark brings Python/Scala processing. Databricks' SQL interface gets better every release.

In 2-3 years, the feature gap will narrow further. The decision will come down to: - Which platform fits your team's existing skills - Which ecosystem (partners, integrations, community) is stronger for your use case - Which pricing model works better for your workload

Don't overthink it. Pick the one that fits your current team and workload. You can always migrate later - it's work, but it's not impossible.

Data platform decisions are just one part of the puzzle. Learn about assessing your organization's overall data maturity.