ETL: Extract, Transform, Load
In computing, extract, transform, load is a three-phase process where data is extracted from a source, transformed, and loaded into a new destination.
What Is ETL (Extract, Transform, Load)?
ETL stands for Extract, Transform, Load. It's a process used in data warehousing that involves:
- Extracting data from various source systems.
- Transforming the data into a suitable format for analysis.
- Loading it into a final target database, data warehouse, or data lake.
Importance of ETL in Data Processing
ETL is vital for data-driven decision making as it:
- Consolidates diverse data into a unified format.
- Ensures data quality and accuracy.
- Facilitates efficient data storage and analysis.
ETL Workflow Overview
The typical ETL workflow consists of three stages:
- Extract: Data is collected from multiple source systems, which can vary in format and structure.
- Transform: The extracted data undergoes cleaning, validation, and conversion to fit the target schema.
- Load: The transformed data is transferred into the data warehouse for storage and analysis.
Key Concepts in ETL
Understanding these key concepts is essential in ETL:
- Data Integration: Combining data from different sources into a single, cohesive view.
- Data Cleansing: Improving data quality by correcting inaccuracies.
- Data Transformation: Converting data from one format or structure into another.
- Batch Processing vs. Real-Time Processing: Deciding between processing data in large batches or in a continuous, real-time manner.