ETL versus ELT in cloud data platforms
data pipeline pattern fluency.
ETL transforms before loading into the target; ELT loads raw first then transforms in the warehouse, leveraging cheap storage and elastic compute.
What's really being asked
This checks whether you understand the ordering of transformation versus loading and why modern cloud platforms shifted toward ELT.
The full answer
In ETL, data is extracted from sources, transformed in a dedicated processing engine, and then the cleaned, modeled result is loaded into the target. This was the norm when warehouses were expensive and inflexible and you only wanted to store final, conformed data. In ELT, data is extracted and loaded in raw form into a powerful cloud warehouse or lake first, and transformation happens afterward inside that system using its elastic compute, usually with SQL or tools like dbt. ELT's advantages in the cloud are significant: object storage and warehouse storage are cheap so keeping raw data is affordable, ingestion is faster because you defer heavy processing, you retain the raw source so you can re-derive new models later without re-extracting, and you exploit the massively parallel compute of engines like BigQuery, Snowflake, or Redshift. The tradeoff is that raw, possibly sensitive data lands in the platform, so governance, access control, and sometimes pre-load masking still matter.
The mistakes people make
Claiming ELT is universally superior with no downsides, ignoring compliance cases where sensitive fields must be masked before loading. Saying the only difference is word order. Forgetting that ELT depends on the target having strong in-place compute. Assuming ETL is obsolete; it remains valid for constrained targets and heavy pre-load cleansing.
What usually comes next
Where does dbt fit. How do you handle PII in an ELT model. When would you still pick ETL.
A concrete example
A team lands raw event JSON directly into Snowflake, then runs dbt models to clean and aggregate it into reporting tables. Months later a new metric is needed, and because the raw data is retained they build it without re-ingesting from the source systems.
Interview question
What is the main reason ELT fits modern cloud data warehouses well?
- a.It avoids storing any raw data
- b.It guarantees sensitive data is masked before loading
- c.It removes the need for any transformation logic
- d.It exploits cheap storage and the warehouse's elastic compute to transform in placeCorrect
Why? this is the answer
ELT loads raw data cheaply then transforms using the warehouse's scalable compute, keeping raw data for later reuse. It actually retains raw data and does not inherently mask sensitive fields before loading.
Just read this? Test yourself on what you have been reading.
Read the original → cloud.google.com
- #cloud
- #etl
- #elt
- #data-engineering
- #data-pipeline
Put your scrolling time to good use
Learn one idea, try a quiz and save useful cards for revision. Tezvyn makes it easy to learn and stay current in your tech field, a few minutes at a time.
The iPhone app is on the way
We are building it. Until it lands, nothing here is held back from you: every interview card, your saved cards, streaks and the job board all work in Safari, plus hundreds of free practice quizzes of thirty questions each. Sign in and it all carries over to the app the day it arrives.
Want it as an icon? Tap Share at the bottom of Safari, then Add to Home Screen. It opens full screen and the cards you have read stay available offline.
We are hiring for this. Open roles that interview on cloud — each one lists the topics its interview covers.
See open roles