Snowflake vs Databricks: Where the Use Cases Overlap and Where They Split

In the past weeks I finished Coursera's Intro to Snowflake for Devs, Data Scientists and Data Engineers, turned it into an interactive mind map, built a Snowflake-style AI data platform and mapped out how to practise the Databricks side on a laptop. The question that came up in every conversation was the same: which one should we use?

The honest answer is that the two platforms overlap more than their marketing suggests, and that the differences that remain are about where you start, not about what is possible. This post lines them up by use case. Snowflake facts come from the course; Databricks facts come from the Databricks documentation. Both platforms change monthly, so treat the tables as a map, not a contract.

Two platforms, two starting points

Snowflake began as a cloud data warehouse. The course calls separating storage from compute "something Snowflake pioneered" and describes four layers: optimized storage, elastic multi-cluster compute, cloud services and Snowgrid. You create warehouses, databases and tables in SQL; files, micro-partitions, encryption and metadata are handled for you.

Databricks began with Apache Spark, the open-source distributed engine its founders created. The platform combines Spark compute with Delta Lake, a table format that adds ACID transactions, time travel and MERGE to Parquet files, and Unity Catalog for permissions, metadata and lineage. The files stay in your own object storage; Databricks supplies the workspace, the compute and the catalog.

Side by side

	Snowflake	Databricks
Origin	Cloud data warehouse, SQL-first	Apache Spark, code-first lakehouse
Where the data lives	Snowflake-managed storage on cloud blob storage; Iceberg for external tables	Your own object storage (S3, ADLS, GCS) as Delta tables
Table format	Micro-partitions managed by the platform	Delta Lake: Parquet plus a transaction log
Compute unit	Virtual warehouse, XS to 6XL, auto-suspend and resume	Spark clusters and SQL warehouses, managed by Databricks
Main languages	SQL first; Python, Java and Scala via Snowpark	Python, SQL and Scala on Spark
Governance	Role-based access control, masking and row-access policies	Unity Catalog: permissions, metadata, lineage
Scheduled transformation	Dynamic tables, streams and tasks	Lakeflow pipelines and jobs
Machine learning	Cortex ML functions, Snowpark ML, Model Registry, Feature Store	MLflow, notebooks, Mosaic AI and model serving
Generative AI	Cortex LLM functions, Cortex Search, Cortex Analyst, fine-tuning	Mosaic AI, model serving
Apps	Streamlit in Snowflake, Native App Framework, Marketplace	Notebooks and dashboards in the workspace
Sharing	Zero-copy shares, Marketplace, Data Clean Rooms	Not covered in this round of notes
Free practice	Trial account, CLI, VS Code extension	Spark plus Delta Lake locally, Free Edition

What they share

Strip the branding and the two platforms agree on the architecture that won the last decade:

• Storage and compute are separate and billed separately. Snowflake sizes warehouses independently of tables; Databricks attaches clusters to tables that live in your buckets.
• SQL and Python are both first-class. Snowflake has SQL worksheets, Snowpark DataFrames and Notebooks; Databricks has Spark SQL, PySpark and notebooks.
• Tables are transactional and have a history. Snowflake's Time Travel queries a table AT a timestamp or BEFORE a query id, with a default retention of 1 day and up to 90 days on Enterprise; Delta Lake gives ACID writes, time travel and MERGE INTO over files.
• Data is layered. The course builds raw, harmonized and analytics schemas; the lakehouse builds bronze, silver and gold. Same idea, different names.
• One catalog governs everything, including the AI features: Snowflake's roles also decide who can call Cortex functions or open a Streamlit app; Unity Catalog does the same job for Databricks assets.
• LLM features live inside the platform rather than in a separate service, so the data does not have to leave to be used by a model.
• Both run on AWS, Azure and Google Cloud.

Where the use cases split

Use case	Leans to	Why
BI dashboards with many concurrent users	Snowflake	Multi-cluster warehouses add clusters when many users query at once, and the result cache serves repeated queries; the course's Streamlit demo reads a view of more than 690 million rows in under 100 lines of code
Large custom transformations in code	Databricks	Spark DataFrames and notebooks are the native way to work; the platform grew out of exactly this
Scheduled transformations without an orchestrator	Snowflake	Dynamic tables refresh on a TARGET_LAG you choose, which the course calls "almost always the right choice" for scheduled transformations with joins
Pipelines that already run on Airflow, Prefect or dbt	Either	Both accept an external scheduler; the course names all three as alternatives to streams and tasks
Streaming ingestion	Either	Snowpipe loads files in under a minute and Snowpipe Streaming plus the Kafka connector load rows in seconds; Databricks runs streaming pipelines in Spark code
Classical ML with scikit-learn or XGBoost habits	Either	Snowpark ML keeps the scikit-learn, XGBoost and LightGBM APIs and trains inside Snowflake; Databricks pairs notebooks with MLflow tracking
Exploratory data science: look, try, plot, compare runs	Databricks	Snowflake's SQL ML functions are four fixed-model statements and every experiment is a billed query; a notebook on a Spark cluster with MLflow is built for the loop
Custom models, GPUs, your own containers	Either	Snowpark Container Services hosts your own model or a partner LLM on Snowflake-managed GPUs; Databricks is a Spark cluster you configure, with Mosaic AI for serving
LLM calls over governed rows from SQL	Snowflake	AI_SENTIMENT, AI_TRANSLATE, AI_CLASSIFY, AI_EXTRACT and AI_COMPLETE run row by row inside a SELECT under the same RBAC as the table
Chat over your own documents (RAG)	Snowflake, or code on Databricks	Cortex Search is a managed retrieval service over a text column, paired with COMPLETE for the answer; on Databricks you assemble the pieces in code
Self-serve questions in natural language	Snowflake	Cortex Analyst answers questions over a semantic model through a REST API; Snowflake Copilot drafts SQL in Snowsight
Sharing data with partners without copies	Snowflake	Shares are zero-copy and read-only, the Marketplace distributes them, and Clean Rooms let two parties analyse combined data without seeing each other's rows
Keeping tables as files you control	Databricks	A Delta table is Parquet files plus a log in your own bucket; any Spark engine can read it
Data apps shipped to business users	Snowflake	Streamlit in Snowflake deploys with one click under existing roles; the Native App Framework sells apps on the Marketplace
Learning for free on a laptop	Databricks	Spark and Delta Lake install with pip; Snowflake needs an account, although the trial covers most of the course

The AI layer: Cortex versus Mosaic AI

Both vendors put models next to the data, but the shape of the offer is different.

	Snowflake Cortex	Databricks Mosaic AI
How you call a model	SQL functions inside SELECT, or the snow cortex CLI	Code in notebooks, model serving endpoints
Ready-made tasks	AI_SENTIMENT, AI_TRANSLATE, AI_EXTRACT, AI_CLASSIFY, AI_PARSE_DOCUMENT, AI_COMPLETE	Build with your own or hosted models
Retrieval for RAG	Cortex Search service over a text column	Assembled in code
Natural language to answers	Cortex Analyst over a semantic model; Copilot in Snowsight	Not in my notes
Custom models	Cortex Fine-Tuning; Snowpark Container Services with GPUs	MLflow, model serving, Spark clusters
Classical ML in SQL	FORECAST, ANOMALY_DETECTION, TOP_INSIGHTS, CLASSIFICATION	MLflow and notebooks
Governance	Same roles as the tables; data never leaves	Unity Catalog

The course puts Cortex on a spectrum from seconds (AI/ML Studio, no code) through minutes (a SQL function) to hours (a container with your own model). Databricks starts at the hours end and works back: you have the whole Spark cluster and MLflow, and you build upward. Which is better depends on who is asking: an analyst who writes SQL gets further on Snowflake; a team that already ships models from notebooks gets further on Databricks.

Where Snowflake stops being an ML and AI exploration tool

One slide from the course sums up my main reservation, and it is the reason I would not put a data-science team's exploration work on Snowflake:

What the slide shows. The entire machine-learning toolbox on the SQL side is four functions. Each one is created the same way: a view reference as INPUT_DATA, a TIMESTAMP_COLNAME, a TARGET_COLNAME, and then a call such as model1!FORECAST(FORECASTING_PERIODS => 3) or model_binary!PREDICT(INPUT_DATA => object_construct(*)). That is the whole training loop. The course adds that FORECAST, ANOMALY_DETECTION and CLASSIFICATION all run a gradient-boosting machine underneath, and that is the only model you get.

Why that is a problem for exploration. Data science is a loop: look at the data, try a transformation, train, plot, compare, go back. From this interface you cannot choose the algorithm, tune it beyond the named parameters, engineer features anywhere except in the SQL of the input view, inspect a learning curve, compare two runs side by side or keep an experiment history. The statement returns an answer, which is exactly right when the question is settled and no help while it is not. Even the course's showcase for real modelling, the Snowpark ML XGBoost demo, reports 730 out of 730 test predictions correct on 7,300 synthetic rows: a demonstration of the API, not a modelling workflow.

The generative side has the same shape. AI_SENTIMENT or AI_COMPLETE return one answer per row with the model as a parameter. The course's row-by-row demo limits itself to 5 rows while the prompt is tuned, because every call is a billed query on a running warehouse and a larger model costs more per token, and Cortex Search would not even run on the trial account because embeddings need a paid one. That is acceptable for scoring governed data in production. It is a poor place to explore: no cheap local iteration, no experiment tracker, and every attempt shows up on the bill.

What Snowflake offers instead, and why it does not change the verdict. Snowpark ML brings scikit-learn, XGBoost and LightGBM-style APIs, a Model Registry and a Feature Store into Snowflake's compute, and the course is right that it beats the stored procedures and UDFs that came before. But it ports the scikit-learn habits into Snowflake's notebooks and warehouses; it does not turn the platform into an exploration environment. Databricks starts from the other end: a notebook on a Spark cluster, MLflow recording every run, the SQL added later. If a team's day is exploration rather than scoring, that difference decides the choice on its own.

A simple decision graph

Learning both without a cloud bill

	Runs locally	Cost	Good for
Apache Spark plus PySpark	Yes	Free	Spark development, DataFrames, Spark SQL
Spark plus Delta Lake	Yes	Free	Lakehouse tables, time travel, MERGE, medallion layers
Databricks Free Edition	No, cloud-managed	Free, with serverless quotas and feature limits	The real workspace, Unity Catalog, Lakeflow
Databricks Connect	Partly: local IDE, remote cluster	Compute may be billed	Developing locally against a real cluster
Snowflake trial account	No, cloud-managed	Free for the trial period	Snowsight, warehouses, Time Travel, cloning, most of Cortex
Snowflake CLI and VS Code extension	Yes, against any account	Free tools	Scripts, CI, editing SQL and Python in your editor
Snowpark locally	Yes, against any account	Free library	DataFrames that run inside Snowflake compute

For the Databricks side, a working local lakehouse is one command away:

python3 -m venv spark-env && source spark-env/bin/activate
pip install pyspark delta-spark jupyterlab
jupyter lab

With that you can practise PySpark transformations, Spark SQL and window functions, Delta tables with time travel, bronze, silver and gold layers, incremental loads with MERGE INTO, and batch or streaming pipelines. What you do not get locally is the platform itself: Photon, Unity Catalog's managed governance and the Databricks-specific services. Databricks Free Edition covers those, within its quotas.

For the Snowflake side, the trial account covers almost the whole course; the one thing that failed on a trial in the course was Cortex Search, because embeddings need a paid account. Locally you install the CLI and the Snowpark library:

pip install snowflake-cli snowflake-snowpark-python
snow connection add && snow connection test
snow sql -q "SELECT CURRENT_VERSION()"

When I needed a Snowflake-shaped environment that starts in seconds with no credentials at all, I built one on DuckDB laid out like a Snowflake account, with bronze, silver and gold schemas, an account_usage schema, masking and row-access policies enforced in SQL, and the same design delivered as real Snowflake DDL for live mode. That project is the Snowflake AI Data Copilot.

What I would choose

If the people asking the questions write SQL and the answers go into dashboards, reports and shared datasets, I start with Snowflake: warehouses that size themselves to the job, dynamic tables instead of a scheduler, and Cortex for sentiment, translation, classification and natural-language questions without exporting a row.

If the people doing the work write Python against large files, train their own models and want the tables to stay in their own buckets as open files, I start with Databricks: Spark is the native language, Delta Lake is the native format, and MLflow and Mosaic AI are the native model tooling. And if the work is exploration rather than scoring, Databricks wins before any other criterion is weighed.

If a company already has both, the useful conversation is not which one but which workload goes where, and the one rule that keeps the two from fighting is open table formats: Iceberg on the Snowflake side, Delta and Parquet on the Databricks side, so that neither platform becomes the only thing that can read the data.

Sources

• Coursera, Intro to Snowflake for Devs, Data Scientists, Data Engineers, by Snowflake (instructor Peter Olson), transcript reviewed on 5 November 2026. Every Snowflake definition, command and figure above is the course's; the whole course is mapped in the Snowflake mind map. Some features were in preview when it was filmed, so check docs.snowflake.com for current availability and pricing.
• Databricks documentation and product pages at docs.databricks.com for Apache Spark, Delta Lake, Unity Catalog, Lakeflow, MLflow, Mosaic AI, Databricks Connect and Free Edition, as summarised in my study notes of November 2026. Quotas and feature limits of the Free Edition change; check the current terms.