Snowflake vs Databricks: Warehouse vs Lakehouse in 2026

Snowflake is a cloud data warehouse. You write SQL, it runs fast, you pay per second of compute. Databricks is a lakehouse — Spark underneath, open table formats on top, notebooks and ML baked in. If your team is analysts writing SQL against structured data, Snowflake will feel more natural. If your team is a mix of data engineers, analysts, and data scientists all touching the same data, Databricks gives you one platform instead of three. The nuance is in where that line blurs.

What's a warehouse and what's a lakehouse?

The architecture difference matters because it dictates what's easy and what's a fight.

           DATA WAREHOUSE (Snowflake)
 
  ┌────────────────────────────────────────────┐
  │          Structured Data (tables)          │
  │                    │                       │
  │           SQL Query Engine                 │
  │         (optimized, managed)               │
  │                    │                       │
  │    Separate Storage ←──→ Elastic Compute   │
  └────────────────────────────────────────────┘
     You bring structured data. Snowflake handles the rest.
 
 
           LAKEHOUSE (Databricks)
 
  ┌────────────────────────────────────────────┐
  │  Raw Files + Structured Tables (your S3)   │
  │         Delta Lake / Unity Catalog         │
  │                    │                       │
  │     ┌──────────────┼──────────────┐        │
  │     │              │              │        │
  │  Spark SQL    Notebooks/ML    Streaming    │
  │  (SQL access)  (Python/Scala)  (Kafka)     │
  └────────────────────────────────────────────┘
     Data lives in YOUR storage. Databricks adds compute + governance.

A warehouse stores your data and gives you a SQL interface. Snowflake owns the storage format, optimizes it behind the scenes, and bills you for compute time. You don't think about files. You think about tables and queries.

A lakehouse sits on top of your cloud storage. Your Parquet and Delta files live in S3, GCS, or ADLS — you own them. Databricks adds a query engine, a metadata layer (Unity Catalog), and ML infrastructure on top. The data never leaves your bucket.

This distinction sounds abstract until you need to do something that crosses the boundary. Want to run a PyTorch training job on your Snowflake data? You're exporting it to S3 first. Want to give a business analyst a point-and-click SQL editor on Databricks? Databricks SQL exists, but it's a newer, thinner experience compared to Snowflake's SQL worksheet.

Which one is better for SQL analytics?

Snowflake. Not close.

The SQL experience in Snowflake is the product. The query optimizer is mature, the web UI is polished, the permission model makes sense to someone who grew up on database RBAC. You create a warehouse (compute cluster), point it at your data, write SQL, get results. Scaling is straightforward — resize the warehouse or add more of them. Each runs independently, so your analyst team's dashboards don't compete with the data engineering team's ELT jobs.

Databricks SQL has improved dramatically. SQL Warehouses (formerly SQL Endpoints) are real, and Photon — their C++ query engine — is legitimately fast. But the experience still feels bolted on. The SQL editor is less feature-rich. The documentation assumes you're comfortable thinking in terms of clusters and Spark configurations. Small things add friction: warehouse startup times, permissions that require Unity Catalog to make sense, catalogs and schemas that don't always surface cleanly in BI tools.

If your team is 80% SQL and 20% Python, Snowflake will make that 80% easier. Snowpark exists for the 20%, but you'll feel the seams.

Which one is better for ML and data science?

Databricks. Not close.

Databricks was Spark. The notebook experience is native — not a feature bolted onto a warehouse. MLflow is built in. You can train a model on the same data your analysts are querying, version it, serve it, monitor it, all without leaving the platform. GPU clusters are a first-class concept. Feature stores, model registries, experiment tracking — it's all there.

Snowflake has been pushing hard here. Snowpark ML, Snowflake Cortex, Container Services — these are real products with real capabilities. But they're young. Snowpark lets you run Python and Scala inside Snowflake, which is genuinely useful for feature engineering. Cortex gives you LLM functions and ML model inference. Container Services lets you run arbitrary Docker containers. The building blocks exist. The integrated ML workflow that Databricks has spent years polishing doesn't, yet.

If your team trains models, runs experiments, and deploys inference pipelines, Databricks is where that work flows naturally.

How do costs compare?

Both are expensive. The billing models are different enough to make direct comparison annoying.

Snowflake charges in credits. One credit is roughly $2-3 depending on your edition and cloud provider. An XS warehouse consumes 1 credit/hour, a small consumes 2, and it doubles from there. You pay only when compute is running — auto-suspend pauses billing. Storage is separate, around $23/TB/month for on-demand. The mental model is clean: warehouse size * runtime = cost.

Databricks charges in DBUs (Databricks Units). Pricing varies by workload type — SQL compute, jobs compute, all-purpose compute, Delta Live Tables — and by tier (Standard, Premium, Enterprise). A DBU for SQL Serverless runs roughly $0.22-0.70 depending on the cloud. Jobs compute is cheaper. All-purpose (interactive notebooks) is the most expensive. Because Databricks sits on your cloud infrastructure, you also pay the underlying VM and storage costs to AWS/Azure/GCP separately.

The real cost difference isn't in the list price — it's in how easy each platform makes it to waste money. Snowflake's per-second billing with auto-suspend means idle compute stops costing you. But a poorly tuned warehouse that handles spiky query loads will auto-scale aggressively. Databricks clusters take 2-5 minutes to start, which pushes teams toward always-on clusters that burn money overnight.

Both platforms need active cost management. Snowflake gives you resource monitors and warehouse-level budgets. Databricks gives you cluster policies and budget alerts. Neither will save you from a developer who spins up an XXL warehouse and forgets about it.

How are they converging?

Both companies are racing toward the same destination from opposite directions.

Snowflake is adding Python. Snowpark lets you write Python DataFrames that execute on Snowflake's engine. Snowflake Cortex adds LLM functions. Container Services runs Docker workloads. The message is clear: you shouldn't need to leave Snowflake for ML.

Databricks is adding warehouse features. Photon makes SQL fast. Unity Catalog adds governance. Databricks SQL gives analysts a SQL-first interface. Delta Lake adds ACID transactions to the lakehouse. The message: you shouldn't need a separate warehouse.

           2020                        2026
 
  Snowflake    Databricks      Snowflake    Databricks
  ┌──────┐     ┌──────┐        ┌──────┐     ┌──────┐
  │ SQL  │     │  ML  │        │ SQL  │     │  ML  │
  │      │     │Spark │        │Python│     │Spark │
  │      │     │ Notebooks     │  ML  │     │ SQL  │
  │      │     │      │        │  AI  │     │  BI  │
  └──────┘     └──────┘        └──────┘     └──────┘
  Pure SQL     Pure Spark      Both adding   Both adding
  warehouse    + ML            ML+Python     SQL+governance

The convergence is real but incomplete. Snowflake's Python support works but doesn't feel native the way Databricks notebooks do. Databricks SQL is fast but doesn't feel as polished as Snowflake's query experience. Heritage shapes user experience for years after the feature gap technically closes.

Side-by-side comparison

How to decide

Pick Snowflake if your primary workload is SQL analytics. Your team writes queries, builds dashboards, connects BI tools, and runs scheduled reports. Some Python, but mostly SQL. You want a managed experience where you don't think about infrastructure. Your data is structured and lives in tables.

Pick Databricks if your team does ML alongside analytics. You need notebooks, experiment tracking, model training on the same data you report from. You want open formats so your data isn't locked into one vendor's storage. You have data engineers who think in Spark and don't mind cluster configuration.

Pick both if you're a large org where the analytics team and the ML team have different needs and different budgets. This is more common than vendor docs suggest. The interop story (via Iceberg tables or external stages) is good enough that running both isn't the operational nightmare it was in 2022.

Where Fastero fits

Fastero connects to both. You plug in your Snowflake credentials or your Databricks SQL Warehouse endpoint, and you get the same experience either way — write SQL queries, build dashboards, set up alerts and triggers. If you're running both platforms, Fastero lets you query across them without exporting data in between. We don't have a preference on which warehouse you pick. The query runs where your data lives.

If you're evaluating whether you even need a warehouse at all, we wrote about running SQL across multiple databases without one. And if you've already picked Snowflake and want to set up event-driven pipelines on it, here's how Snowflake triggers work.

FAQ

Is Snowflake a data warehouse or a data lake? Snowflake is a cloud data warehouse. It stores structured and semi-structured data in a proprietary format with full SQL support. It recently added Iceberg table support for open-format interop, but the core experience is a managed warehouse, not a lake.

Can Databricks replace Snowflake? For some teams, yes. Databricks SQL with Photon handles analytical SQL workloads well, and it's getting better each quarter. But if your team is all SQL analysts using BI tools like Tableau or Looker, Snowflake's connector ecosystem and query experience are still ahead. Replacing Snowflake with Databricks makes sense when you also need ML and your team is comfortable with cluster-based infrastructure.

Which is cheaper, Snowflake or Databricks? Neither is categorically cheaper. Snowflake's pricing is simpler to predict — credits times runtime. Databricks has lower list prices for some workload types, but you also pay cloud infrastructure costs separately, which makes budgeting harder. The cheapest option is whichever one your team optimizes. Unoptimized spend on either platform gets expensive fast.

What is a lakehouse architecture? A lakehouse combines the raw storage of a data lake (files in cloud storage, open formats) with the governance and query performance of a data warehouse (ACID transactions, schema enforcement, SQL access). Databricks coined the term with Delta Lake. The idea is to avoid maintaining a separate lake and warehouse by putting a structured layer on top of your object storage.

Can I use both Snowflake and Databricks together? Yes, and many large organizations do. A typical pattern is Snowflake for BI and SQL analytics, Databricks for ML and data science. They can share data via Iceberg tables, external stages, or Delta Sharing. The operational overhead of running both is real, but for teams with genuinely different workload profiles, it can be the right call.

Does Snowflake support Python and ML? Snowflake has Snowpark (Python/Scala/Java DataFrames that execute on Snowflake), Cortex (LLM functions and ML model inference), and Container Services (run Docker containers). These are production-ready but newer than Databricks' ML stack. For light ML work and Python-based transformations, Snowpark is solid. For serious ML engineering with experiment tracking, model serving, and GPU training, Databricks is further along.

Try Fastero free — connect Snowflake, Databricks, or both, and query your data from one place. No credit card required.