✨ When should I use a Snowflake transformation?

A Snowflake transformation runs your SQL against a Snowflake database that Keboola manages for you. You write SELECT / CREATE TABLE statements; Keboola takes care of the warehouse, the staging area, and moving results back to Storage. This page explains what that means and when it is the right choice. To build one, follow the how-to; for exact limits and syntax rules, see the reference.

What it is

Like every transformation, a Snowflake transformation operates on an isolated copy of your data, not on Storage directly:

1. Input mapping copies the Storage tables you name into a temporary staging schema.
2. Your SQL script runs against that staging schema.
3. Output mapping writes the resulting tables back to Storage.

Because it works on a copy, you can rename or restructure Storage tables without breaking the script, and a failed run never corrupts your source data.

Why Snowflake

Snowflake is a cloud data warehouse, which removes most of the operational burden of traditional databases:

• No database administration — no servers, vacuuming, or patching to manage.
• No indexes, sort keys, distribution styles, or column compression to design and tune.
• Easy scaling — increase the backend size when a job needs more power, without rewriting anything.
• Simple data types and a familiar SQL dialect.
• Strong processing power and throughput for large joins and aggregations.

Being a managed cloud service, Snowflake also ships continuous updates; occasionally that means behavioral changes worth tracking in the release notes.

When to use it (and when not to)

Choose a Snowflake transformation when:

• Your logic is naturally expressed in SQL — joins, aggregations, filtering, denormalizing, integrity checks.
• Your data is tabular and you want set-based processing close to where the data already lives.
• You want to scale up heavy jobs simply by changing the backend size.

Consider a different backend when:

• You need procedural code, custom libraries, or ML — use a Python or R transformation.
• Your project runs on a different warehouse — Keboola also offers BigQuery, DuckDB, and Oracle transformations. The concepts on this page are the same; the SQL dialect and limits differ.

Things to understand up front

Two Snowflake behaviors trip people up; both are detailed in the reference:

• Case sensitivity. Snowflake folds unquoted identifiers to upper case, but Keboola creates tables and columns in their original case. Quote your identifiers ("my_column") so they match — see identifier case sensitivity.
• Everything lands as character data. Storage stores columns as character types, so values are cast to char on output — and ARRAY, OBJECT, and VARIANT must be cast explicitly. See working with data types.

Understanding these two points early saves most of the debugging time newcomers spend on Snowflake transformations.