ETL
Extract, Transform, Load
A data integration process that extracts data from sources, transforms it to fit operational needs, and loads it into a destination system like a data warehouse.
In-Depth Explanation
ETL (Extract, Transform, Load) is a data integration pattern for moving data between systems. It's fundamental to data warehousing, analytics, and data migration projects.
ETL stages:
- Extract: Pull data from source systems (databases, APIs, files)
- Transform: Clean, validate, restructure, enrich data
- Load: Write to destination (warehouse, database, lake)
Common transformations:
- Data type conversion
- Deduplication
- Null handling
- Joining/merging datasets
- Aggregation and calculations
- Data validation and quality checks
- Format standardisation
ETL vs ELT:
- ETL: Transform before loading (traditional)
- ELT: Load raw, transform in destination (modern cloud approach)
ETL tools:
- Traditional: Informatica, Talend, SSIS
- Modern: dbt, Fivetran, Airbyte, Stitch
Business Context
ETL powers business intelligence and analytics. Without ETL, data stays siloed and unusable for cross-system reporting and analysis.
How Clever Ops Uses This
We implement ETL pipelines for Australian businesses to consolidate data for analytics, migrate between systems, and enable data-driven decision-making.
Example Use Case
"Nightly ETL pulling sales from POS, inventory from ERP, and customers from CRM into a data warehouse for unified reporting dashboards."