ETL vs ELT — When to Use Which Approach for Data Pipelines

ETL and ELT are two fundamental approaches to moving and transforming data. While traditional ETL transforms data before loading, modern ELT leverages the compute power of cloud warehouses.

What Are ETL and ELT¶

ETL (Extract, Transform, Load) extracts data from sources, transforms it in a dedicated environment, and loads it into the target. ELT reverses the order — data is loaded raw and transformed in the target system.

When to Choose ETL¶

• GDPR and compliance — masking data before storage
• Limited target performance — on-premise DB without elastic compute
• Complex business logic — specialized transformation tools

When to Choose ELT¶

• Cloud warehouses — Snowflake, BigQuery, Redshift
• Flexibility — raw data for various transformations
• Iterative development — transformations change without re-extraction

# ETL vs ELT — When to Use Which Approach for Data Pipelines
import pandas as pd
from sqlalchemy import create_engine

# EXTRACT
raw = pd.read_sql('SELECT * FROM orders', source_engine)

# TRANSFORM
df = raw.copy()
df['total_czk'] = df['total_eur'] * 25.2
df = df[df['status'] != 'cancelled']

# LOAD
df.to_sql('dim_orders', target_engine, if_exists='append')

Modern Hybrid Approach¶

In practice, both approaches are combined. Sensitive data goes through ETL, the rest through ELT. dbt handles transformations in the target warehouse.

Summary¶

ETL for regulated environments, ELT for modern cloud. Most organizations combine both approaches based on the nature of the data.