Enrich records with related data from another table via JOIN operations
Fetch and integrate user details based on user ID from a reference dataset
Handling null values: Ensure appropriate handling and replacement of missing values.
Insufficient Data Profiling and Cleansing
Data profiling involves analyzing the pakistan rcs data source data to understand its structure, content, and relationships. Profiling of both source and target data identifies data types, formats, ranges, patterns, distributions, anomalies, and quality issues.
Importance for data transformation requirements:
Accurate understanding of source data: Profiling provides insights into the actual state of the data, preventing assumptions that could lead to incorrect transformation logic.
Identification of data quality issues: Detects missing values, duplicates, outliers, and inconsistencies that must be addressed in the transformation requirements.
Informing transformation logic: Helps define precise transformation rules, mappings, and handling of exceptional cases based on actual data characteristics.
Importance of data cleansing: Data cleansing involves correcting or removing inaccurate, incomplete, or irrelevant data from the source datasets to improve data quality.