ELT (Extract, Load, Transform)
ELT is a modern data pipeline pattern that extracts data from sources, loads it as-is into a target system (usually a cloud warehouse), then applies transformations using the warehouse's native capabilities.
ELT reverses the traditional ETL sequence: data lands in the warehouse in its raw form, then SQL transformations create cleaned, conformed tables. This approach leverages cloud warehouse compute power and eliminates separate transformation infrastructure. Tools like Fivetran or cloud storage services handle the simple extract and load; dbt performs transformations. ELT is faster and more cost-effective than ETL because warehouse engines are optimized for scale, transformations are expressed in familiar SQL, and analytics teams can modify transformations without waiting for specialized tools.
ELT emerged as cloud data warehouses demonstrated they could handle complex transformations more efficiently than dedicated ETL engines. Modern cloud warehouses have columnar storage, vectorization, and optimization that make them faster at transformation than separate processing layers. ELT also aligns with the Modern Data Stack philosophy of modular, specialization tools.
Organizations using ELT typically separate raw data (unchanged from sources) from conformed data (after transformations), enabling audit trails and the ability to replay transformations if business rules change. The main trade-off is storage cost: raw data is stored until transformed, and intermediate tables proliferate as more transformations are needed.
Key Characteristics
- ▶Extracts data from sources in original format
- ▶Loads data into warehouse without transformation
- ▶Transforms using warehouse SQL and tools (dbt, stored procedures)
- ▶Separates raw and transformed data layers
- ▶Enables rapid iteration on transformations
- ▶Leverages warehouse scalability for transformation power
Why It Matters
- ▶Reduces time-to-analytics by eliminating separate transformation engines
- ▶Reduces infrastructure costs by using warehouse compute instead of dedicated tools
- ▶Enables faster iteration on transformations through SQL familiarity
- ▶Reduces specialist dependency by using standard SQL instead of proprietary tools
- ▶Scales efficiently with cloud warehouse auto-scaling
- ▶Enables data scientists and analysts to own transformation logic
Example
A SaaS company uses ELT: Fivetran extracts from Salesforce, Stripe, and PostgreSQL daily, loads raw JSON and tables directly to Snowflake. dbt transforms: customer_staging normalizes Salesforce contacts, revenue_staging aggregates Stripe charges, intermediate tables join customer and revenue data, fct_revenue_daily materializes the final fact table. Analysts query fct_revenue_daily for dashboards; data engineers iterate on dbt models when business rules change.
Coginiti Perspective
Coginiti is built around ELT as the default pattern. CoginitiScript's block-based language enables modular, reusable transformations that execute in-warehouse after data lands, taking advantage of the target platform's native SQL engine across Snowflake, Databricks, BigQuery, Redshift, and 20+ other connectors. Publication support for tables, views, Parquet, CSV, and Iceberg gives teams flexibility over how transformed results are materialized, while the analytics catalog ensures all transformation logic is versioned, reviewed, and governed.
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