DA0-002 Data Concepts and Environments Practice Question
A data engineer is designing a data pipeline for a retail company. The source system is an OLTP database that records sales transactions. The target is a data warehouse used for reporting. The engineer is evaluating whether to use ETL or ELT. Which three factors would favor using ELT over ETL? (Select THREE)
⚠ Common exam trap
Watch out — candidates often confuse the direction of data flow, mistakenly thinking that ELT requires transformations before loading, when in fact ELT defers transformations until after data is in the warehouse.
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
✓
The business analysts need access to raw data for ad-hoc exploration
ELT loads raw data into the warehouse first, allowing business analysts to perform ad-hoc exploration directly on the source data without pre-transformation. This flexibility is a key advantage of ELT over ETL, where transformations are applied before loading.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The transformation logic requires proprietary functions not available in the warehouse
Why it's wrong here
If the warehouse lacks the needed functions, ETL with external tools is better.
- ✓
The business analysts need access to raw data for ad-hoc exploration
Why this is correct
ELT loads raw data, allowing analysts to explore it before transformation.
- ✓
The target data warehouse has massive compute power (e.g., Snowflake) that can handle transformations efficiently
Why this is correct
ELT leverages the warehouse's compute for transformations.
- ✗
Data must be cleansed and validated before loading into the warehouse
Why it's wrong here
Requiring cleansing and validation before loading describes the transformation-heavy paradigm of ETL, where data undergoes processing in a staging area prior to reaching the target. This requirement necessitates an intermediate compute layer to enforce schema constraints or data quality rules during transit. You would select this approach if the destination system lacks the computational power to handle heavy transformations or if strict governance requires sanitising sensitive information before it enters the warehouse environment.
- ✓
The source data volume is very large and the warehouse can scale resources on demand
Why this is correct
ELT can handle large volumes by scaling warehouse resources.
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