How does Snowflake support ELT patterns and how does it compare to ETL?

ELT (Extract, Load, Transform) loads raw data into Snowflake first, then transforms it inside using SQL or Snowpark. ETL (Extract, Transform, Load) transforms data in an external tool before loading it. Snowflake's MPP engine and separation-of-storage-from-compute make it strongly suited for ELT — the transformation step is where Snowflake's power is applied, not a place to avoid.

ELT advantages with Snowflake: no external transformation infrastructure to manage; transformations can access the full history of raw data at any time (Time Travel); warehouse can be sized specifically for heavy transform runs; SQL models are easy to version-control with dbt; Snowpark handles transformations that require Python or ML libraries.

Typical Snowflake ELT stack:

• Ingestion: Fivetran, Airbyte, or Snowpipe pulls data into a raw schema.
• Staging: dbt or Snowpark cleans and casts the raw data into typed staging tables.
• Transformation: dbt models join, aggregate, and enrich staging data into semantic layer tables.
• Serving: BI tools (Tableau, Looker, Power BI) query the semantic layer through a dedicated reporting warehouse.

-- ELT pattern: transform raw JSON into a typed table inside Snowflake
CREATE OR REPLACE TABLE orders_clean AS
SELECT
  raw:order_id::NUMBER        AS order_id,
  raw:customer_id::VARCHAR    AS customer_id,
  raw:amount::DECIMAL(10,2)   AS amount,
  raw:order_ts::TIMESTAMP_NTZ AS order_ts
FROM raw.orders_raw
WHERE raw:status::VARCHAR = 'completed';

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