CompTIA Data+ (DA0-002)Data MiningMedium
A data architect is setting up a new data pipeline to ingest real-time sensor data. The data needs to be available for immediate analysis in a data lake, but complex transformations, aggregations, and data quality checks will be performed later by downstream processes. Which data integration approach would be most suitable for this scenario?
- AETL (Extract, Transform, Load)
- BOLAP (Online Analytical Processing)
- CELT (Extract, Load, Transform)
- DData Virtualization
Show answer & explanationAnswer & explanation
Correct answer: C. ELT (Extract, Load, Transform)
ELT is ideal for large volumes of real-time data where raw data needs to be loaded quickly into a data lake, and transformations can be performed later using the data lake's processing power.
Why the other options are wrong
- A. ETL performs transformations before loading, which can be slower for real-time data ingestion.
- B. OLAP is for analytical querying of data warehouses, not a data integration approach.
- D. Data Virtualization provides a unified view of disparate data without physical movement, not ideal for ingesting raw data into a lake.
ELT (Extract, Load, Transform)
A data integration approach where data is first extracted from sources, loaded into a target system (like a data lake), and then transformed within that system.
- Favored for big data and cloud environments.
- Allows for faster data loading.
- Leverages the processing power of the target system for transformations.
Memory trick: Fast data flow needs smart processing.