ELT
Also: Extract, Load, Transform
ELT (Extract, Load, Transform) is a data integration pattern where raw data is loaded into a target system first, then transformed inside it using the platform's compute power.
What it is
ELT stands for Extract, Load, Transform. It is a data integration approach where data is pulled from source systems, loaded in its raw form into a target platform (typically a cloud data warehouse or lakehouse), and only then transformed into clean, modeled datasets. This reverses the order of the older ETL pattern, where transformation happens before loading.
The shift to ELT was driven by the rise of scalable, low cost cloud storage and powerful in warehouse compute engines (such as columnar query engines). When transformation can run cheaply and quickly inside the destination, there is little reason to do it beforehand in a separate processing layer.
Why it matters
- Speed of ingestion: Raw data lands fast because loading is decoupled from transformation logic.
- Flexibility: Keeping raw data means you can re-model it later without re-extracting from sources.
- Scalability: Transformation leverages the elastic compute of modern warehouses instead of a fixed ETL server.
- Auditability: The original raw data is preserved, which helps with debugging, reprocessing, and compliance.
- Separation of roles: Engineers manage ingestion; analysts and analytics engineers own transformations using SQL based tools.
For a Chief Data Officer, ELT supports a governed, single source of truth while letting teams iterate on business logic without bottlenecking on a central pipeline team.
How it is used in practice
A typical ELT stack combines:
1. Extract and Load tools that connect to APIs, databases, and files, replicating data into the warehouse.
2. A cloud warehouse or lakehouse as the central store.
3. A transformation framework (commonly SQL based) that builds layered, tested, documented models.
Transformations are usually organized in layers: raw, staging (cleaned), and marts (business ready tables for reporting and AI features).
Concrete example
A marketing team wants a unified view of campaign performance:
- Extract: Pull raw rows from an ad platform API, a CRM, and web analytics.
- Load: Drop all three raw datasets, unmodified, into the warehouse.
- Transform: Run SQL models that standardize date formats, deduplicate leads, join spend to revenue, and produce a `campaign_roi` table.
If a new metric is needed next quarter, analysts simply add a model on top of the raw data already loaded, with no new extraction required.
Frequently asked questions
What does ELT stand for?
ELT stands for Extract, Load, Transform. Data is pulled from source systems, loaded raw into a target platform such as a cloud data warehouse or lakehouse, and only then transformed into clean, modeled datasets.
What is the difference between ELT and ETL?
The difference is the order of the last two steps. In ETL, data is transformed in a separate processing layer before being loaded; in ELT, raw data lands first and transformation runs inside the destination platform, using its compute engine.
Why did ELT replace ETL in most modern data stacks?
Because cloud storage became cheap and in-warehouse compute became powerful, notably with columnar query engines. When transformation runs quickly and inexpensively inside the destination, maintaining a separate ETL server before loading loses its purpose.
What are the components of an ELT stack?
Three: extract and load tools that connect to APIs, databases and files to replicate data; a cloud warehouse or lakehouse as the central store; and a transformation framework, usually SQL based, that builds layered, tested and documented models.
How are ELT transformations organized inside the warehouse?
In layers: raw data as loaded, staging models where it is cleaned, and marts containing business-ready tables for reporting and AI features. For a marketing use case, staging would standardize date formats and deduplicate leads, while a mart would join spend to revenue to produce a campaign_roi table.