DA0-002 Data Concepts and Environments Practice Question
A retail company processes daily transactions. The current system transforms data before loading it into the data warehouse. The volume is growing rapidly, and they want to load raw data first to reduce processing time. Which approach should they adopt?
⚠ Common exam trap
It's easy for candidates to confuse ETL and ELT, assuming that 'transform before load' (ETL) is always faster, but the question explicitly states the goal is to reduce processing time by loading raw data first, which directly points to ELT.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ELT (Extract, Load, Transform)
(ELT) because the company wants to load raw data first and then transform it later, reducing initial processing time. ELT leverages the power of modern data warehouses to perform transformations after loading, which is ideal for rapidly growing volumes of raw transaction data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change data capture (CDC)
Why it's wrong here
CDC captures only changed rows from source systems; it does not restructure the warehouse load into an extract-raw-then-transform pattern. It is tempting because it reduces extraction volume, and it would be correct when the goal is minimising source impact rather than deferring transformation.
- ✗
ETL (Extract, Transform, Load)
Why it's wrong here
ETL transforms data before loading, which is the existing approach the company wants to replace with raw loading. It is tempting as the established warehouse pattern, and it would be correct where transformation must occur before data lands, such as strict schema or cleansing requirements.
- ✓
ELT (Extract, Load, Transform)
Why this is correct
ELT loads raw data into the warehouse first, then transforms it there using the warehouse's compute. This satisfies the stem's requirement to load raw data first and reduce processing time, unlike ETL, which transforms before loading.
- ✗
Data replication
Why it's wrong here
Replication copies and continuously synchronises data between stores; it does not restructure the load pipeline, so raw data still arrives transformed. It is tempting because replication suits distributing read workloads or disaster recovery across regions, where maintaining duplicate copies matters rather than eliminating pre-load transformation.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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