ELT
ELT (Extract, Load, Transform) is a data integration process that extracts data from source systems, loads it into a target platform, and transforms it there for analysis and reporting. It is commonly used with cloud data warehouses and data lakehouses that can process large datasets within the destination. ELT allows organizations to retain raw data while preparing different datasets for analytics, business intelligence, and machine learning.
How Does ELT Work?
ELT moves data into its destination before applying the main transformation logic.
- Extract: Collect data from databases, applications, APIs, files, or other sources.
- Load: Transfer the extracted data into a target platform, often preserving much of its original structure.
- Transform: Clean, combine, standardize, and model the data within the destination to meet analytical or business requirements.
For example, an organization can load raw sales, customer, and website event data into a cloud data warehouse. It can then transform those datasets into reporting tables for customer analysis and revenue tracking.
Why Is ELT Important?
ELT takes advantage of the processing capabilities of modern data platforms and provides flexibility in how data is prepared.
- Retain raw data: Preserve source data for different analytical requirements, subject to storage and governance policies.
- Scale data processing: Use the computing resources of the target platform to transform large datasets.
- Support multiple use cases: Create different analytical datasets from the same ingested source data.
- Accelerate ingestion: Load data before completing every transformation step.
- Adapt analytical models: Update transformation logic as reporting requirements evolve, provided the necessary source data is retained.
ELT still requires data quality controls, access permissions, cost management, and reliable transformation workflows.
What Is the Difference Between ELT and ETL?
The main difference is where transformation takes place.
| Aspect | ELT | ETL |
|---|---|---|
| Full form | Extract, Load, Transform | Extract, Transform, Load |
| Transformation | After loading | Before loading |
| Processing location | Usually the target platform | Often an intermediate processing layer |
| Raw data | Can be retained in the target | May be transformed before reaching the target |
| Common use | Cloud warehouses and lakehouses | Workflows requiring transformation before loading |
Both approaches can support reliable analytics. The choice depends on the destination’s processing capabilities, data sensitivity, transformation needs, and operational constraints.
Related Data Concepts
- ETL
- Data Ingestion
- Data Integration
- Data Warehouse
- Data Lakehouse
- Data Transformation
- Data Modeling
- Data Pipeline
Related Data Tools and Resources
Explore the best ETL Tools to prepare data before loading, Data Integration Tools to connect source systems, and Data Pipeline Tools to orchestrate data movement and transformation workflows.
Frequently Asked Questions
What does ELT stand for?
ELT stands for Extract, Load, Transform. It describes a process that loads data into a destination before transforming it there.
What is an example of ELT?
A business can extract customer and transaction records from operational systems, load them into a cloud data warehouse, and then transform them into reporting tables within the warehouse.
Why is ELT popular in cloud data warehouses?
Cloud data warehouses can provide scalable computing resources for processing data after it has been loaded. This can make it practical to retain raw data and build multiple transformed datasets in the same platform.
Is ELT better than ETL?
Neither approach is always better. ELT is useful when the target platform can efficiently transform loaded data, while ETL is useful when data must be transformed before it reaches the destination.