Databricks vs Snowflake in 2026: An Honest Comparison for Data Teams

Last updated: October 2025

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Choose Snowflake for SQL-heavy analytics, BI workloads, and data sharing where your team is primarily SQL-skilled — it requires zero infrastructure management and excels at concurrency. Choose Databricks for ML/AI workloads, streaming, and complex data engineering where your team writes Python/Spark — it offers better notebook experience and MLflow integration. Many teams use both: Databricks for data engineering and ML, Snowflake for analytics and BI. Here’s the detailed comparison across 8 dimensions. - Snowflake started as a SQL data warehouse and expanded toward data engineering and ML, while Databricks started as a Spark-based data processing engine and expanded toward SQL analytics and governance. That origin story matters, because it shows up in where each platform is strongest (and where it's still catching up).

Architecture: Lakehouse vs Managed Warehouse

Databricks is a lakehouse built on top of your cloud storage (S3, ADLS Gen2, or GCS). Your data stays in your cloud account as Delta Lake tables (Parquet files with a transaction log). Databricks provides the compute layer - Spark clusters that read and write to your storage. You own the data and the storage costs; Databricks charges for compute (DBUs).

Snowflake is a fully managed service with its own storage layer. Data is loaded into Snowflake's proprietary format (micro-partitions, columnar, compressed). You don't manage storage directly - Snowflake handles compression, clustering, and replication. Compute is separated from storage via virtual warehouses that can be spun up and down independently. For more on how Snowflake manages these workloads, see our guide on managing compute workloads for ETL vs analytics.

What this means in practice: With Databricks, you have full control over your data files - you can read them with any tool that understands Parquet/Delta. With Snowflake, your data is in Snowflake's format and you access it through Snowflake's interfaces. Databricks gives more flexibility; Snowflake gives more simplicity.

Query Performance

Snowflake wins on ad-hoc SQL queries. Its micro-partition pruning, result caching (queries return instantly if the underlying data hasn't changed), and auto-suspend/resume make it incredibly responsive for analyst workflows. A well-tuned Snowflake warehouse returns most dashboard queries in under 2 seconds.

Databricks wins on iterative ML workloads. Spark keeps intermediate results in memory across iterations, which matters a lot when you're training models, running feature engineering pipelines, or processing unstructured data (text, images, logs). Databricks SQL (formerly SQL Analytics) has closed the gap on ad-hoc query performance with Photon engine, but Snowflake's query optimizer is still more mature for complex SQL.

Data Engineering

Databricks: Notebooks with Python, Scala, SQL, or R. Delta Live Tables (DLT) for declarative pipeline definitions. Workflows for job scheduling and orchestration. The notebook experience is excellent for iterative development - you can explore data, prototype transforms, and productionize them in the same environment.

Snowflake: Streams and Tasks for CDC and scheduling. Dynamic Tables for declarative, auto-refreshing materialized views. Snowpark for Python/Java/Scala UDFs and stored procedures. Snowflake's SQL-native approach is simpler if your transforms are expressible in SQL. For teams coming from a SQL background, the learning curve is much lower.

dbt works great with both. If you're using dbt (and you probably should be), the experience is nearly identical on both platforms. dbt models compile to SQL, and both engines execute SQL well. See our dbt + Snowflake guide for specifics.

ML and AI Capabilities

Databricks has a clear lead here. MLflow (model tracking, versioning, deployment) is native and mature. Feature Store is built in. Model Serving provides real-time inference endpoints. You can go from notebook experimentation to production model serving without leaving the platform. The ML Runtime includes pre-configured GPU clusters with PyTorch, TensorFlow, and HuggingFace libraries.

Snowflake is catching up fast. Cortex provides built-in ML functions - forecasting, anomaly detection, sentiment analysis, and LLM inference (including access to Llama, Mistral, and Arctic models) - all callable via SQL. Snowpark ML lets you train scikit-learn and XGBoost models inside Snowflake without exporting data. But for custom deep learning, complex model pipelines, or anything involving GPUs, Databricks is still the stronger choice.

Governance

Databricks Unity Catalog provides centralized governance across workspaces: table-level and column-level access control, data lineage, audit logging, and row-level security. It's workspace-aware and integrates with your cloud provider's identity systems.

Snowflake Horizon bundles governance features: dynamic data masking, row access policies, object tagging, data lineage, and access history. Snowflake's governance model is simpler to set up because everything is in one account - no workspace federation to worry about. For a deeper look, see our guide on data access control strategies.

Cost Model

Databricks charges in DBUs (Databricks Units). The price per DBU varies by workload type (Jobs Compute, All-Purpose Compute, SQL Compute, Delta Live Tables, Model Serving) and by cloud provider. A DBU on AWS Jobs Compute costs differently than on Azure All-Purpose Compute. This makes cost prediction harder - you need to model your specific workload mix.

Snowflake charges in credits. One credit = one Snowflake warehouse running for one hour (at XS size). Larger warehouses consume more credits per hour (S=2, M=4, L=8, etc.). The pricing is simpler to understand and predict. But here's the gotcha: if you don't configure AUTO_SUSPEND aggressively (we recommend 60 seconds for dev, 300 seconds for production), idle warehouses burn credits for nothing.

Storage costs: With Databricks, you pay your cloud provider directly for storage (S3, ADLS). With Snowflake, storage is billed separately at roughly $23-40/TB/month depending on region and edition.

Ecosystem and Tooling

dbt: Works great with both. No meaningful difference. Airflow: Both have well-maintained providers. Databricks has a slightly richer Airflow integration with notebook-level triggering. Fivetran/Airbyte: Both support Snowflake and Databricks as destinations. Terraform: Both have mature Terraform providers for infrastructure-as-code.

When to Pick Snowflake

• Your workload is 80%+ SQL analytics and BI dashboards
• Your team is SQL-first (analysts who don't write Python)
• You want a simpler operational model with less infrastructure to manage
• You need cross-cloud data sharing between organizations
• Your governance requirements center on SQL-level access control

When to Pick Databricks

• You're doing heavy ML model training and serving
• You need Spark for processing unstructured data (text, images, logs)
• You want one platform for data engineering, analytics, and ML
• Your team is comfortable with Python/Scala and notebooks
• You want to own your data in open formats (Delta Lake/Parquet) on your cloud storage

When to Use Both

This isn't a cop-out - it's a real pattern we see in production. Run your SQL analytics and BI workloads in Snowflake (where the query optimizer and caching make analysts happy). Run your ML training, feature engineering, and unstructured data processing in Databricks (where Spark and MLflow shine). Share data between them via external tables on shared cloud storage. dbt and Airflow can orchestrate across both.

Key Takeaways

• Snowflake = managed SQL warehouse, great for analytics teams. Databricks = Spark lakehouse, great for ML and engineering teams.
• For ad-hoc SQL, Snowflake's query optimizer and result caching are hard to beat. For iterative ML, Databricks' in-memory Spark engine and MLflow integration win.
• Cost comparison isn't apples-to-apples: Databricks DBU pricing is complex, Snowflake credits are simpler. Model your specific workload.
• Both platforms are converging - Snowflake is adding ML, Databricks is improving SQL. But each still has a clear home-turf advantage.
• Using both is a valid architecture, not a failure to decide. Many enterprise teams do exactly this.

Frequently Asked Questions