How do you ensure data quality across multiple datasets in a lakehouse?

• A. Rely on data producers to be correct
• B. Quality checks only in BI layer
• C. No automated checks
• D. Global data quality rules, schema validation, cross-dataset checks, data profiling runs, and alert when quality thresholds fail.

Answer: (D)

In a lakehouse, data quality must be enforced everywhere data moves and lives, not left to chance with the producers or only checked at the reporting layer. The strongest approach uses automated, centralized quality controls that span all datasets. Global data quality rules establish uniform expectations across the whole lakehouse—what values are allowed, valid formats, acceptable nulls, and referential consistency. Schema validation checks incoming data against the defined structure so bad shapes or types are caught before they propagate. Cross-dataset checks validate relationships and coherence between related datasets, such as matching keys, consistent timestamps, and aligned dimension data. Data profiling runs continuously examine data distributions, detect anomalies, missing values, and drift, helping you spot evolving quality issues. Alerting when these quality thresholds fail ensures timely remediation and keeps the data trustworthy for downstream analytics and governance.

Relying on data producers to be correct leaves you exposed to upstream mistakes, and performing quality checks only in the BI layer means bad data can reach dashboards before issues are discovered. There’s also no automated checks, which would let problems slip by unnoticed.