A practical comparison of Databricks and Snowflake for engineers - architecture differences, workload fit, pricing realities, and where each platform excels.

If you're evaluating cloud data platforms in 2026, Databricks and Snowflake are likely at the top of your list. Both have evolved significantly from their origins - Snowflake from a cloud-native data warehouse, Databricks from a managed Spark platform - and now compete across overlapping territory. The marketing from both sides makes them sound interchangeable, but the architectural foundations and sweet spots remain distinct.

This comparison cuts through the noise. For standard SQL analytics and data warehousing, the two platforms are closer than ever in capability and performance. But when you push into heavy data engineering, streaming, or ML workloads, the differences become material. Understanding these distinctions will save you from costly migrations later.

Architecture: Lakehouse vs Data Warehouse

The fundamental architectural difference shapes everything else. Snowflake implements a multi-cluster shared data architecture with three distinct layers: a proprietary micro-partitioned columnar storage layer, independent virtual warehouses (XS through 6XL) for compute, and a services layer handling metadata and query optimization. This design delivers strong workload isolation - a runaway query in one warehouse won't affect others - and requires minimal tuning. Given some hints like partition keys, Snowflake handles many optimizations automatically.

Databricks takes the lakehouse approach, built on Delta Lake running on your cloud provider's object storage (S3, ADLS, or GCS). Your data stays in open Parquet-based formats with ACID transactions, schema evolution, and time travel layered on top. Compute runs on Spark clusters or serverless SQL warehouses. The Photon engine - a C++ vectorized execution layer - accelerates SQL and DataFrame operations significantly over standard Spark.

The practical implication: with Databricks, your data remains in formats readable by any engine supporting Delta Lake or Iceberg. You're not locked in. With Snowflake, data historically lived in proprietary storage, though they've added Iceberg table support and open-sourced Polaris Catalog to address lock-in concerns. Still, most Snowflake workloads use the native format. For organizations paranoid about vendor lock-in or needing multi-engine access to the same data, Databricks' open approach is compelling. For those prioritizing simplicity and managed infrastructure, Snowflake's opacity is a feature, not a bug.

Workload Fit: SQL Analytics vs Data Engineering

Snowflake was built SQL-first, and it shows. The query optimizer is mature, concurrent query handling is excellent, and the experience for analysts using BI tools is polished. Dynamic Tables provide declarative incremental transformations without writing procedural code. If your primary workload is analysts running dashboards, ad-hoc queries, and scheduled reports, Snowflake delivers with minimal friction. The learning curve is gentle - anyone comfortable with SQL can be productive immediately.

Databricks speaks Python and Spark natively. While SQL support has improved dramatically (the serverless SQL warehouses are genuinely competitive), the platform's DNA favors data engineers writing transformations in PySpark or notebooks. This flexibility is powerful when you need it - custom logic, complex multi-stage pipelines, or anything beyond SQL's expressiveness - but it demands more technical depth from your team. Lakeflow Declarative Pipelines (formerly Delta Live Tables) provides managed ETL with autoscaling, though it's still more involved than Snowflake's equivalent features.

For data sharing and collaboration, Snowflake has a clear lead. Zero-copy sharing lets you expose live data to other Snowflake accounts without duplication. The Marketplace enables data monetization. Data Clean Rooms support secure multi-party analytics. Databricks has Delta Sharing, which works across platforms (not just Databricks-to-Databricks), but the ecosystem and tooling around Snowflake's sharing capabilities are more developed.

Some Data Engineering features do exist in Snowflake - notebooks, and a batch processor called Snowpark which is effectively a Snowflake version of Apache Spark. But for any real data processing and ML workloads (see below), Databricks definitely has an advantage.

Pricing and Performance Reality

Both platforms use consumption-based pricing, but the mechanics differ. Snowflake charges per credit ($1.50-4.00 depending on commitment and edition), with warehouse sizes consuming 1-8+ credits per hour. Storage runs around $23/TB/month. Databricks charges per DBU (Databricks Unit), ranging from $0.22 for jobs light compute to $0.70 for serverless SQL, but you also pay your cloud provider separately for the underlying infrastructure - VMs, storage, networking. This dual-billing model makes Databricks cost estimation harder; infrastructure can add 50-200% on top of DBU charges.

For standard analytical workloads - dashboards, reporting, SQL queries against structured data - independent assessments (including Fivetran's 2024 benchmarks) put the platforms in a "near-tie for performance." Snowflake's Gen2 warehouses (GA May 2025) brought significant improvements, roughly 2x faster execution and 4.4x better DML performance. Databricks countered with Photon and serverless SQL optimizations. Cherry-picked benchmarks favor whichever vendor commissioned them; real-world performance depends on your specific query patterns, data volumes, and concurrency requirements.

Where Snowflake often costs more: the simplicity premium. Automatic optimization, minimal tuning, and managing everything come at a price. Organizations report 20-40% higher costs for equivalent workloads compared to well-optimized Databricks deployments. The keyword is "well-optimized" - Databricks requires more expertise to tune effectively, and poorly configured clusters can burn money fast. If your team lacks deep Spark knowledge, Snowflake's predictability may be worth the premium.

Where Databricks Pulls Ahead: ML and Heavy Compute

For machine learning and AI workloads, Databricks operates in a different league. The platform provides an integrated ML stack: MLflow for experiment tracking and model lifecycle management (now at version 3 with significant improvements), Mosaic AI for building RAG applications and AI agents, native GPU cluster support for training, and model serving endpoints for deployment. You can train models on the same data you use for analytics without moving anything.

Snowflake has responded with Cortex AI - LLM functions, anomaly detection, forecasting, and Cortex Analyst for natural-language-to-SQL. These are useful for augmenting analytics with AI capabilities. But there's a fundamental difference: Snowflake runs pre-built models and managed services; Databricks lets you train custom models, fine-tune foundation models, and deploy arbitrary ML code. If you're building differentiated ML products, not just adding AI features to dashboards, Databricks is the only real choice.

Streaming tells a similar story. Databricks' Structured Streaming processes data as it arrives with exactly-once guarantees, writing directly to Delta tables that are immediately queryable. Change Data Feed enables CDC patterns natively. Snowflake's Snowpipe Streaming has improved significantly - the high-performance variant (GA September 2025) supports up to 10 GB/second ingestion with sub-10-second latency - but it's still fundamentally about getting data into Snowflake quickly for later querying, not processing streams with complex logic. For true stream processing with transformations, joins, and windowing, Databricks remains the platform.

Heavy ETL workloads with complex transformations, large-scale data processing that benefits from distributed computing, and anything requiring GPUs - these are Databricks territory. The Spark foundation handles petabyte-scale datasets that would strain Snowflake's architecture. Unity Catalog (now open-sourced) provides unified governance across structured data, unstructured files, ML models, and AI assets - a breadth Snowflake's Horizon governance doesn't match.

Making the Decision

The honest answer: for a straightforward cloud data warehouse powering BI dashboards and analyst SQL queries, both platforms work. Snowflake will likely be easier to adopt and manage; Databricks may cost less with sufficient expertise. Evaluate based on your team's skills and tolerance for operational complexity.

The differentiation emerges at scale and sophistication. If your roadmap includes serious ML/AI development, real-time streaming applications, or workloads that benefit from open data formats and multi-engine flexibility, Databricks is the strategic choice. Snowflake cannot match its capabilities for these use cases today, and the architectural gaps aren't closing quickly.

For organizations where data primarily flows into dashboards and reports - even sophisticated ones - Snowflake's simplicity and SQL-native experience make it the pragmatic choice. Don't overcomplicate your stack for hypothetical future ML projects. If those projects materialize, you can integrate Databricks for ML workloads while keeping Snowflake for analytics. Many organizations run both.

The worst outcome is choosing based on marketing or defaulting to what a vendor rep recommended. Define your actual workloads, assess your team's technical depth honestly, and let those factors drive the decision.