ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) differ in where the transformation happens. ETL transforms on a separate compute layer before loading into the warehouse; ELT loads raw into the warehouse and transforms with the warehouse’s own compute.
The cloud-warehouse era (Snowflake, BigQuery, Redshift) made warehouse compute cheap and elastic which made ELT dramatically more efficient than ETL and pushed it into the default modern pattern. There are still cases where ETL wins; this page covers both.
ETL Defined
Extract from source; transform on a separate compute layer (Informatica, Talend, custom Python / Spark) where you clean, join, derive; then load the transformed result into the warehouse. The warehouse only stores cleaned, modeled data.
ELT Defined
Extract from source; load raw into the warehouse landing zone; then transform inside the warehouse using SQL (often via dbt). The warehouse stores raw plus transformed; transformation runs on warehouse compute. See What is Data Warehousing for how warehouse compute architecture supports this pattern.
Why ELT Took Over
Three reasons: cloud warehouse compute became cheap and elastic, so the cost argument against ELT collapsed; in-warehouse SQL is easier to write, test, and version than middle-tier transformation code; and dbt productized the workflow.
For most modern analytical workloads, ELT is the default. See What Data Warehousing Allows Organizations to Achieve for why the warehouse is now the right place to run transformations.
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Side-by-Side Comparison
|
Dimension |
ETL |
ELT |
|
Transform location |
Separate compute |
In the warehouse |
|
Tooling |
Informatica, Talend, Spark |
dbt + warehouse SQL |
|
Raw data stored? |
Often no |
Yes (landing zone) |
|
Idempotent reruns |
Harder |
Easier |
|
Cost on cloud |
Higher (separate compute) |
Lower (warehouse uses elastic) |
|
Best for |
Legacy / heavy pre-load cleaning |
Modern cloud analytics |
When ETL Still Wins?
ETL still wins when: source data is too sensitive to land raw in a shared warehouse (PII tokenization before load); when transformation requires non-SQL compute (heavy Python, ML feature engineering at scale); when the warehouse is cost-constrained for transformation workloads; or when regulatory / contractual rules require pre-load transformation.
Honest ETL has a place; it just isn’t the default anymore. Centric builds ELT (and where appropriate, ETL) pipelines through its data engineering and warehousing service.
Frequently Asked Questions
What is the difference between ETL and ELT?
Transform location. ETL transforms before load; ELT loads raw then transforms in the warehouse.
Is ELT always better?
Mostly, on cloud. ETL still wins for some compliance, PII, and heavy non-SQL transformation cases.
Do we need both?
Some programs do. ELT for the bulk of analytical workloads; ETL for pre-load PII handling or non-SQL transformation. Hybrid is common.
What about reverse-ETL?
Different pattern reverse-ETL sends warehouse-modeled data back into operational tools. It's about activation, not transformation. See What Data Warehousing Allows Organizations to Achieve for how the modeled layer feeds these downstream consumers.
Conclusion
ETL was the right pattern when compute was scarce and warehouse storage was expensive. Cloud warehouses changed both, and ELT became the default. Most modern data programs are ELT-led, with ETL reserved for specific cases.
The practical decision is workload-driven match the pattern to the data shape, compliance need, and cost envelope, not to a religious preference. At Centric, we build ELT-first pipelines designed for modern cloud warehouses and apply ETL where the workload genuinely demands it
