MHP

ETL vs. ELT – Understanding the Pipeline Patterns

ETL Extract → Transform → Load

Source
Transform
Server
Warehouse
Transform data before loading into the warehouse
Requires a separate processing server (e.g., SSIS, Informatica)
Schema must be defined upfront before load
Best for:
Structured data, legacy on-prem systems, compliance-heavy pipelines

ELT Extract → Load → Transform

Source
Cloud
Warehouse
Transform
Load raw data first, transform inside the warehouse
Faster ingestion — no upfront transformation bottleneck
Scales with cloud compute — leverages warehouse power
Best for:
Cloud data warehouses, diverse data types, high-volume / semi-structured data
Modern trend: ELT dominates with cloud platforms like Databricks, Snowflake, and Fabric — leveraging scalable compute and decoupled storage.