ETL vs ELT Pipelines: What is the Difference?
Learn the difference between ETL and ELT data pipelines, why ELT suits cloud warehouses, and the trade-offs of each approach.
Expected Interview Answer
ETL (Extract, Transform, Load) transforms data in a separate processing engine before loading it into the target system, while ELT (Extract, Load, Transform) loads raw data into the target first and performs the transformation there, using the target's own compute power.
ETL was the standard when target systems (traditional warehouses) had limited compute, so a dedicated transformation server cleaned, joined, and reshaped data before it ever touched the warehouse, keeping only modeled data inside it. ELT became popular once cloud warehouses and lakehouses (Snowflake, BigQuery, Redshift) offered cheap, elastic compute, so raw data is loaded first and SQL-based transformations run directly inside the target, which is simpler to operate, preserves raw data for reprocessing, and scales transformation compute independently. The trade-off is that ELT pushes governance and cost control into the target system, while ETL front-loads that control but requires maintaining a separate transformation layer.
- ETL: clean, modeled data only, and target stays smaller
- ELT: raw data is preserved for reprocessing and audits
- ELT: leverages elastic cloud warehouse compute for transforms
- ETL: transformation logic is centralized outside the warehouse
AI Mentor Explanation
Think of ETL like a groundstaff crew that fully prepares the pitch off-site — mowing, rolling, marking — before it is ever installed at the stadium, so only the finished, ready pitch reaches the ground. ELT is more like installing raw turf directly at the stadium first, then having the groundstaff mow, roll, and mark it on-site using the stadium's own equipment. Both end with a playable pitch, but ELT keeps the raw turf available in case a different marking is needed later, while ETL never kept that raw material around.
Step-by-Step Explanation
Step 1
Extract
Pull raw data from source systems such as application databases, APIs, or logs.
Step 2
Choose transform order
In ETL, transform data in a separate processing engine before loading; in ELT, load raw data first.
Step 3
Load
Write the (transformed or raw) data into the target warehouse or lakehouse.
Step 4
Transform in-target (ELT only)
Run SQL-based transformations directly inside the target system, using its own elastic compute.
What Interviewer Expects
- Correct expansion and ordering of both acronyms
- Understanding of why ELT became popular with cloud warehouses
- Awareness of the raw-data-preservation benefit of ELT
- Mention of trade-offs around governance and compute location
Common Mistakes
- Mixing up which step (transform vs load) comes second in each approach
- Claiming ELT is always strictly better without discussing trade-offs
- Forgetting to mention cloud warehouse elasticity as the driver for ELT adoption
- Not mentioning that ETL keeps raw data out of the target system
Best Answer (HR Friendly)
“ETL cleans and transforms data on a separate system before loading only the finished result into the warehouse, while ELT loads the raw data into the warehouse first and then transforms it there using the warehouse's own processing power. ELT has become more popular with cloud warehouses because it is simpler to operate and keeps raw data around for reprocessing.”
Code Example
-- Step 1 (Extract + Load): raw events are already loaded as-is
-- into a staging table inside the warehouse
-- Step 2 (Transform): the transformation runs as plain SQL
-- using the warehouse's own compute, not an external engine
CREATE TABLE clean_orders AS
SELECT
order_id,
CAST(order_date AS DATE) AS order_date,
customer_id,
ROUND(total, 2) AS total
FROM raw_orders_staging
WHERE total IS NOT NULL;Follow-up Questions
- What tools are commonly used for ELT (e.g. dbt) versus traditional ETL?
- Why did ELT become more popular with the rise of cloud data warehouses?
- What are the governance risks of loading raw, untransformed data into a warehouse?
- How do incremental loads differ from full loads in a pipeline?
MCQ Practice
1. In an ELT pipeline, where does the transformation step happen?
ELT loads raw data into the target first, then performs transformations there using the target's own compute.
2. What is a key reason ELT became popular with cloud data warehouses?
Elastic, relatively cheap compute in modern cloud warehouses makes it practical to run transformation logic directly inside the target.
3. Which pipeline approach keeps raw, untransformed data available in the target for future reprocessing?
ELT loads raw data first, so the original untransformed data remains available for future reprocessing or audits.
Flash Cards
What does ETL stand for? — Extract, Transform, Load — transformation happens before loading into the target.
What does ELT stand for? — Extract, Load, Transform — raw data is loaded first, then transformed inside the target.
Why did ELT gain popularity? — Cloud warehouses offer cheap, elastic compute that makes in-target transformation practical.
What is one benefit of ELT over ETL? — Raw data is preserved in the target system, enabling future reprocessing or audits.