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Answer: Use Azure Data Factory to apply a custom transformation to impute or estimate the missing values based on other data.
Handling missing data is crucial for maintaining data quality. Ignoring or dropping the data is not a recommended approach. Filling in missing values with a default value may introduce bias or inaccuracies. A better approach is to use a data transformation tool like Azure Data Factory to apply custom logic for imputing or estimating the missing values based on other available data, thus preserving the integrity and completeness of the dataset.
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You are working on a data pipeline that ingests data from various sources into Azure Data Lake Storage. Some of the incoming data has missing values. How would you handle this scenario to ensure data quality and completeness?
A
Ignore the records with missing values and continue processing the rest of the data.
B
Fill in the missing values with a default value, such as zero or a placeholder string.
C
Use Azure Data Factory to apply a custom transformation to impute or estimate the missing values based on other data.
D
Drop the entire dataset and request a new dataset from the data provider.