Data transformation errors (45%): Numerous reports and articles emphasize that transformation is one of the most error-prone stages due to the complexity of logic, schema changes, and data manipulation. Papers like “Big Data Testing Techniques” and industry blogs like Datafold’s article on testing data pipelines (Datafold report) highlight that transformation steps often introduce logic bugs, incorrect joins, and aggregation issues.
Data ingestion errors (15%): Data ingestion issues arise laos rcs data from incorrect configuration of sources, network problems, and file formatting issues. Sources like Thoughtworks’ article on pipeline testing mention ingestion as a significant challenge, though less frequent than transformation errors.
Schema mismatches (10%): Problems often arise when data schema evolves or differs from expectations, as seen in the Data Pipeline Quality study (see Cornell University arXiv Laboratory). These errors typically occur during schema validation or component testing stages.
Data quality issues (20%): Many organizations experience quality issues such as missing or corrupt data. Such issues are common due to faulty input data, transmission corruption, or improper transformation handling.
Integration errors (5%): Integration issues (e.g., between systems or APIs) are less frequent but still notable, mainly when using external systems like APIs.
Chart 1: Survey – A breakdown of data errors in data pipeline workflows by cause
Training a machine learning model to test data transformations in a data pipeline involves understanding transformation logic, identifying potential errors, and leveraging labeled data to build a model that can detect these errors automatically. This approach improves data quality and ensures that transformed data is accurate and reliable. Data transformation testing focuses specifically on the correctness of transformations (see Table 1).