ELT vs ETL in dbt: Key Differences and When to Use Each

In dbt, ELT means Extract, Load, then Transform data inside the warehouse using SQL models, while ETL means Extract, Transform, then Load data before it reaches the warehouse. dbt is designed primarily for ELT workflows, focusing on transformations after loading data.

Quick Comparison

Here is a quick side-by-side comparison of ELT and ETL in the context of dbt workflows.

Factor	ETL	ELT (dbt)
Order of Steps	Extract → Transform → Load	Extract → Load → Transform
Where Transformation Happens	Before loading into warehouse	Inside the data warehouse
Tool Focus	ETL tools like Informatica, Talend	dbt and SQL in warehouse
Data Latency	Usually slower due to pre-processing	Faster with warehouse power
Flexibility	Less flexible for ad-hoc changes	Highly flexible with SQL models
Complexity	More complex pipelines	Simpler, modular SQL transformations

Key Differences

ETL stands for Extract, Transform, Load. It means data is pulled from sources, transformed outside the warehouse, then loaded in a clean form. This approach often uses specialized ETL tools and can be slower because transformations happen before loading.

ELT, used by dbt, extracts data and loads it raw into the warehouse first. Then, transformations happen inside the warehouse using SQL models. This leverages the warehouse's processing power and allows more flexible, modular transformations.

In dbt, you write SQL SELECT statements as models that transform raw data already loaded. This contrasts with ETL where transformations happen in separate tools before loading. ELT with dbt simplifies pipelines and supports easy version control and testing.

Code Comparison

Example of an ETL transformation using Python before loading data:

python

import pandas as pd

# Extract data from source
raw_data = pd.read_csv('source.csv')

# Transform data
raw_data['full_name'] = raw_data['first_name'] + ' ' + raw_data['last_name']
clean_data = raw_data[['id', 'full_name', 'email']]

# Load transformed data to warehouse (simulated)
clean_data.to_csv('clean_data.csv', index=False)

Output

A CSV file 'clean_data.csv' with columns: id, full_name, email

ELT Equivalent in dbt

Equivalent transformation in dbt using SQL model:

sql

-- models/clean_data.sql
SELECT
  id,
  first_name || ' ' || last_name AS full_name,
  email
FROM {{ ref('raw_data') }}

Output

A transformed table 'clean_data' inside the warehouse with columns: id, full_name, email

When to Use Which

Choose ETL when you need to transform data before loading due to legacy systems, limited warehouse power, or strict data governance requiring clean data upfront.

Choose ELT with dbt when you want to leverage your data warehouse's power, prefer modular SQL transformations, and need flexible, maintainable pipelines that are easy to test and version control.

Key Takeaways

dbt is built for ELT workflows, transforming data inside the warehouse using SQL.

ETL transforms data before loading, often using external tools and can be slower.

ELT with dbt offers more flexibility, simpler pipelines, and faster iteration.

Use ETL if your environment requires pre-loading transformations or has limited warehouse resources.

Use ELT with dbt to leverage modern cloud warehouses and modular SQL transformations.