Why consider basic data cleansing when loading data into Fabric lakehouse?

Why consider basic data cleansing when loading data into Fabric lakehouse?

• A. To reduce data load size and processing time
• B. To enforce data privacy and security measures
• C. To simplify data discovery in the catalog
• D. To ensure data quality and consistency

Answer: (D)

Cleansing data before loading into Fabric lakehouse centers on ensuring data quality and consistency. By validating values, standardizing formats, removing duplicates, correcting errors, and addressing missing data according to business rules, you prevent bad or inconsistent records from skewing analytics, dashboards, and models. This leads to more trustworthy insights and stronger governance across datasets. While cleansing can indirectly affect data volume or processing time, the primary purpose is to guarantee data quality and uniformity, not primarily to reduce load size, enforce privacy, or simplify catalog discovery. Privacy needs, masking, and metadata-driven catalog improvements are separate concerns handled through other processes and practices.

Cleansing data before loading into Fabric lakehouse centers on ensuring data quality and consistency. By validating values, standardizing formats, removing duplicates, correcting errors, and addressing missing data according to business rules, you prevent bad or inconsistent records from skewing analytics, dashboards, and models. This leads to more trustworthy insights and stronger governance across datasets. While cleansing can indirectly affect data volume or processing time, the primary purpose is to guarantee data quality and uniformity, not primarily to reduce load size, enforce privacy, or simplify catalog discovery. Privacy needs, masking, and metadata-driven catalog improvements are separate concerns handled through other processes and practices.