A company uses a 'Data Lake' architecture to store unstructured customer feedback logs alongside structured transaction data. When auditing the completeness of data retrieval for analysis, what is a primary challenge the auditor should anticipate compared to a traditional Data Warehouse?

Answer options:

Schema-on-read requires validation of data structure at the time of query, not at ingestion.

Data Lakes cannot store structured data like SQL tables.

Data Lakes enforce strict referential integrity upon entry.

Data Lakes are only accessible via proprietary vendor tools.

How to approach this question

Contrast Data Warehouse (Schema-on-write, clean, structured) with Data Lake (Schema-on-read, raw, messy).

Full Answer

A.Schema-on-read requires validation of data structure at the time of query, not at ingestion.✓ Correct

Data Lakes store raw data. The structure (schema) is applied when the data is read (schema-on-read). This creates a risk that the query logic might not correctly interpret the raw data, leading to completeness or accuracy issues during analysis.

Common mistakes

Assuming Data Lakes are just bigger Data Warehouses.

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