Data type conversions: Ensure fields are correctly converted to the desired data types.
Convert string dates into a date-time format and verify
Change a numeric field into its string representation
String manipulations: Validate operations performed on string data types.
Replace null values with default values or averages
Validate the forward or backward filling of gaps in time series data
Confirm the deletion netherlands rcs data or flagging of records with missing essential fields
Data splitting and merging: Validate the segmentation or combination of datasets.
Split a dataset into training and testing sets based on a ratio or condition.
Merge multiple datasets based on common fields
Validate the vertical or horizontal partitioning of a dataset
Error and outlier handling: Confirm and manage anomalies in the data.
Identify and flag or remove statistical outliers in a numeric field
Validate the correct logging of transformation errors for troubleshooting
Check for the replacement of unrealistic or incorrect values (e.g., negative age)
Validation and verification: Check the quality and integrity of the data
Verify that records not conforming to the schema are flagged or corrected.
Ensure that records with invalid references are identified and handled.
Transactions with negative amounts are rejected or corrected.
Designing test scenarios for data transformation functions ensures accurate and consistent processing, laying the groundwork for high-quality analytics and decision-making. Understanding specific functions and potential issues ensures robust, accurate, and reliable data pipeline processes.