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 standardization
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 for US companies. Without ETL, data stays siloed across Salesforce, NetSuite, Shopify, and other systems, unusable for cross-system reporting and IRS compliance.
How Clever Ops Uses This
We implement ETL pipelines for American businesses to consolidate data from popular US platforms 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."