What is a data lake and how differs from a data warehouse in MDC3?

• A. A data lake stores raw, diverse data; a data warehouse stores structured, curated data for analysis; MDC3 uses both depending on analytics needs.
• B. A data lake and a data warehouse are the same.
• C. A data lake stores only text data; a data warehouse stores only images.
• D. A data lake requires schema upfront.

Answer: (A)

Data lakes and data warehouses are two ways to store data for analytics with different purposes. A data lake stores raw, diverse data in its native form—structured, semi-structured, and unstructured—without requiring upfront schema. It uses schema-on-read, so you decide how to interpret the data when you query it, which makes it flexible for exploration and data science. A data warehouse stores structured, curated data that has been cleaned and integrated to support fast, reliable queries for reporting and business intelligence, with a defined schema at load time (schema-on-write) and strong governance.

In MDC3, you typically use both depending on analytics needs: ingest a wide variety of data into a data lake for discovery and experimentation, then transform and organize a refined subset in a data warehouse for production-grade analysis and reporting. This captures how organizations balance flexibility with performance and governance.

Other options don’t fit: a data lake is not limited to text, and a data warehouse is not limited to images; and data lakes do not require upfront schema (they rely on schema-on-read rather than schema-on-write).