Introduction to ETL

This document is a draft version and subject to further review and revision. Please refrain from distributing or relying on its contents as final.

ETL stands for Extract, Transform, and Load, which is a data integration process used to move data from source systems to a target destination. ETL involves three main phases: extracting data from source systems, transforming it into a suitable format, and loading it into a target system.

ETL Workflow

1. Extract:
   • Data is collected from various sources, such as databases, files, APIs, and external providers.
   • Extraction can be full (all data) or incremental (based on a filter).
2. Transform:
   • Data is cleaned, validated, and transformed to fit the target schema.
   • Operations include data cleansing, aggregation, filtering,, and data type conversion.
   • Business rules are applied to ensure data consistency and accuracy.
3. Load:
   • Transformed data is loaded into the target destination, such as a data warehouse or database.
   • Loading methods can be full (replace existing data) or incremental (update or append).

Benefits of ETL

• Data Integration: ETL integrates data from disparate sources into a unified format.
• Improved Data Quality: ETL processes clean and standardize data, reducing errors.
• Efficient Analysis: Transformed data is ready for analysis and reporting.
• Historical Data: ETL can handle historical data, enabling trend analysis.
• Automation: ETL processes can be automated, reducing manual efforts.