What is Extract, Transform, Load?ETL
Extract, Transform, Load (ETL) is the data integration process that pulls data from multiple source systems, reshapes it into a consistent structure, and loads it into a target environment such as a data warehouse or business intelligence platform.
ETL is the backbone of analytics. Without it, dashboards reflect whatever shape the source system happens to produce — fragile, inconsistent, and slow. With it, business intelligence environments reliably produce the same number for the same question every time. Modern ETL stacks include Power Query, Fabric Dataflows, dbt, Azure Data Factory, and event-driven pipelines. The newer ELT pattern (load first, transform inside the warehouse) is increasingly common for cloud-native stacks. Codnity Data implements ETL pipelines specifically for Power BI environments, including incremental refresh, transformation logic, and governed semantic models.
What it includes
- Source system connectors (accounting, CRM, ERP, custom APIs)
- Transformation logic (data cleaning, deduplication, reshaping)
- Target environment provisioning (warehouse, semantic model)
- Incremental refresh and scheduling
- Lineage documentation and version control
- Error handling and observability
How it works
Audit data sources
Inventory every source system, its API, refresh cadence, and known data-quality issues. Output a connection matrix.
Design the model
Star schema or snowflake. Clear fact and dimension tables. Documented business rules. The target shape drives transformation design.
Build pipelines
Implement extraction, transformation, and load with version control, tests, and incremental refresh. dbt is the modern default.
Govern and iterate
Document lineage, set up alerts on broken pipelines, and tie ownership to teams. Untended ETL becomes silent reporting debt.
Frequently asked
ETL or ELT — which is right for me?
ETL transforms data before loading; ELT loads first and transforms inside the warehouse. ELT is faster to iterate when storage is cheap (cloud warehouses). ETL is preferred when transformation needs to happen near the source for compliance or performance reasons.
How often should ETL pipelines run?
Daily for most reporting; hourly for operational dashboards; near-real-time only when the use case justifies the cost. Match cadence to the decisions the data drives.
What does an ETL implementation cost?
Project cost depends on number of source systems, transformation complexity, and target environment. We scope transparently after a discovery session — typical Power BI ETL builds run 4–8 weeks.