Comprehensive and Detailed In-Depth Explanation:
When encountering issues where reports are incomplete or data appears to be missing after ETL (Extract, Transform, Load) processes, it's essential to assess the quality and structure of the data.Data profilingis the process of examining the data available in an existing data source and collecting statistics and information about that data. This practice helps in understanding the data's condition, identifying anomalies, and ensuring that the data conforms to the expected patterns.
Option A:Cross-validation
* Rationale:Cross-validation is a statistical method used to estimate the skill of machine learning models.
It is primarily used in predictive modeling to assess how the results of a statistical analysis will generalize to an independent dataset. While valuable in model evaluation, it doesn't address issues related to missing or incomplete data in ETL processes.
Option B:Data profiling
* Rationale:Data profiling involves analyzing the data for accuracy and completeness. By performing data profiling, analysts can identify missing values, inconsistencies, and anomalies within the dataset.
This process is crucial for diagnosing issues that arise during ETL operations, such as incomplete data loads or transformation errors.