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ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration processes that move data from multiple sources into a central repository such as a data warehouse or data lake. These workflows are essential for building reliable pipelines that feed machine learning and AI systems with high-quality, structured data.
What is ETL?
ETL stands for Extract, Transform, Load, a process in which data is first extracted from multiple systems, transformed into a consistent format, and then loaded into a destination such as a data warehouse. ETL is typically used when transformations must occur before the data is stored.
- Extract: Pull data from databases, APIs, files, or external systems.
- Transform: Clean, normalise, and enrich the data to ensure consistency and accuracy.
- Load: Move the processed data into the target storage or analytics system.
What is ELT?
ELT stands for Extract, Load, Transform, a modern variation where raw data is loaded directly into the destination system first, and transformations are performed afterwards. This approach leverages the computational power of modern cloud data warehouses like Snowflake, BigQuery, or Databricks.
- Extract: Collect raw data from multiple sources.
- Load: Move data into a data lake or cloud storage environment.
- Transform: Apply business rules and data preparation steps within the warehouse.
ETL vs. ELT: Key differences
| Aspect | ETL | ELT |
|---|---|---|
| Processing Location | Outside the warehouse | Inside the warehouse |
| Performance | Slower for large datasets | Optimised for cloud scalability |
| Complexity | Higher due to multiple environments | Lower with unified architecture |
| Use Case | Legacy systems, on-premise environments | Modern cloud data platforms |
| Transformation Timing | Before loading | After loading |
Why ETL/ELT matters for AI and analytics
AI and business intelligence tools depend on clean, reliable data. ETL and ELT workflows ensure that data is consistent, traceable, and ready for downstream analysis. They reduce time-to-insight, prevent errors, and improve the accuracy of AI model predictions.
- Improved data quality: Ensures only accurate and complete data enters your systems.
- Data governance: Maintains visibility and compliance across sources and transformations.
- Operational efficiency: Automates repetitive data preparation tasks.
- Scalability: Handles growing volumes of structured and unstructured data.
Choosing between ETL and ELT
The choice depends on your organisation’s architecture and goals. ETL works best for complex, regulated transformations that must occur before data enters the warehouse. ELT is ideal for modern, cloud-native setups where compute resources can handle transformations efficiently after loading.
Both play a crucial role in preparing data for Data Quality Management, Machine Learning, and enterprise-scale analytics.