CompTIA Data+ DA0-002 (V2) Practice Question

The correct answer is to use the ELT (Extract, Load, Transform) process. In an ELT model, raw data is first extracted from the sources and loaded directly into the target system, such as a cloud data warehouse. The transformation of the data then occurs within the warehouse itself, leveraging its powerful and scalable processing engine. This approach is ideal for handling large volumes of unstructured or semi-structured data, as it allows for fast data ingestion and provides data scientists with the flexibility to access and transform the raw data for various analytical purposes.

The ETL (Extract, Transform, Load) approach is less suitable. While ETL is effective for ensuring data quality before loading, the upfront transformation step can become a bottleneck when dealing with massive datasets, slowing down data ingestion. Furthermore, it does not fully utilize the computational power that modern cloud data warehouses are designed to provide. Choosing ETL because it is more cost-effective is often incorrect in a cloud context, as ELT can reduce costs by simplifying the data stack and eliminating the need for a separate transformation server. The claim that ELT is better for data privacy is also false; ETL is typically preferred for strict compliance scenarios because it can cleanse or mask sensitive data before it enters the warehouse.