CompTIA Data+ (DA0-002)Data MiningMedium
A data engineering team is setting up a new data pipeline for their e-commerce platform. They need to move raw transactional data from a production database into a data lake, then clean and transform it for analytical reporting in a data warehouse. The process involves ingesting data as-is, then performing complex transformations and aggregations later. Which data integration approach is best suited for this scenario?
- AOLTP (Online Transaction Processing)
- BOLAP (Online Analytical Processing)
- CELT (Extract, Load, Transform)
- DETL (Extract, Transform, Load)
Show answer & explanationAnswer & explanation
Correct answer: C. ELT (Extract, Load, Transform)
ELT is ideal when raw data needs to be loaded into a data lake first, enabling schema-on-read flexibility and allowing transformations to occur within the powerful data warehouse environment later. This aligns with the scenario's requirement to ingest data 'as-is' and perform complex transformations afterwards.
Why the other options are wrong
- A. OLTP is a type of database system optimized for transactional operations, not a data integration approach.
- B. OLAP is a type of database system optimized for analytical queries, not a data integration approach.
- D. ETL performs transformations before loading, which doesn't fit the 'ingest as-is then transform later' requirement.
ELT (Extract, Load, Transform)
ELT is a data integration process where data is extracted from source systems, loaded directly into a target data system (like a data lake or data warehouse), and then transformed within that target system.
- Leverages the processing power of the target system.
- Ideal for large volumes of data and schema-on-read environments.
- Data is available sooner in the target system for exploration.
Memory trick: Extract, Load, then Transform within the big lake!