In MDC3 analytics, when should data be stored in a data lake versus a data warehouse?

• A. Use data lake for raw, diverse data; data warehouse for structured, curated data ready for analysis.
• B. Data lake stores structured, curated data; data warehouse stores raw, unprocessed data.
• C. Use data lake for high-security data; data warehouse for public data.
• D. Always store all data in a data warehouse.

Answer: (C)

The key idea is matching storage to how data will be used and controlled. A data lake serves as a centralized, scalable place to keep raw, diverse data from many sources, with governance and security controls that can be applied consistently at scale. Storing high-security data there allows you to enforce strict access, encryption, masking, and auditing while preserving the ability to process the data later in its native form. A data warehouse, by contrast, is designed for structured, cleaned, and modeled data that’s ready for fast analytics and easy sharing with business users. Placing data that is intended for broad, routine analysis—often less sensitive or public—in a warehouse makes it easier to query, port into dashboards, and distribute across teams. So, aligning high-security data with the data lake and reserving the data warehouse for curated, shareable datasets fits how these environments are optimized to be used and governed.

Other options mix up raw versus curated data or push all data into one solution, which would sacrifice either flexibility for raw data, performance for analytics, or governance for sensitive information.