# ELT Pipeline

> Load raw data first, then transform inside the data warehouse using cheap compute.

- **Category**: Data Science
- **Subcategory**: Data Pipelines
- **Canonical URL**: https://designpattern.fyi/patterns/elt_pipeline/

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## Description
**Context**: The shift from on-premise data warehouses to cloud warehouses (Redshift, BigQuery, Snowflake) changed what was architecturally feasible. Compute and storage became cheap and elastically scalable.


## Use Cases
Most AI/ML data platforms built today should use ELT. Load raw data to bronze, transform with dbt. The ability to reprocess from bronze when bugs are found is worth the storage cost.



## Implementation Example

```python
# ELT Pipeline Example
raw_data = load_to_warehouse("raw_events")
transformed = dbt_transform(raw_data)
```



## Trade-offs


### Advantages

- - Enables on-demand backfill

- - Reprocess from raw data when bugs found

- - Cheap compute for transformations




### Considerations & Drawbacks

- - Higher storage costs for raw data

- - Requires data warehouse with ELT support







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**Reference**: [Original Source](https://engineersofai.com/docs/data-engineering/foundations/Data-Pipeline-Patterns)