Validate and Profile Data
Before developing the data pipeline, review your sample or historical data provided. This step helps you understand the data structure and quality, setting a solid foundation for your pipeline development. Tools like Jupyter Notebooks are highly effective to conduct data validation and profiling. These critical processes ensure data quality, accuracy, and consistency in big data pipelines.
Here is a comprehensive list of some common checks you can run on data:
Data completeness:
- Check for missing values in all columns.
- Verify that all required fields are present.
- Calculate the percentage of missing data per column.
Data accuracy:
- Identify outliers and anomalies in numeric data.
- Verify data against predefined rules or reference sources.
- Cross-reference data with external sources for accuracy.
Data consistency:
- Ensure uniformity in data formats (for example: dates, currency).
- Check for inconsistent or conflicting values within columns.
Data integrity:
- Validate primary and foreign key relationships between tables.
- Detect and resolve data duplication issues.
Data uniqueness:
- Check for duplicate records within the dataset.
- Appropriately handle any duplicate entries.
Data range and valid values:
- Verify that data falls within expected ranges.
- Ensure that categorical columns contain valid values.
Data transformation and cleansing:
- Apply necessary data transformations (for example, unit conversions).
- Cleanse data by removing or correcting inconsistencies and errors.
Data profiling:
- Calculate basic statistics (mean, median, etc.) for numeric columns.
- Determine cardinality for categorical columns.
- Identify the most and least common values in each column.
Data type validation:
- Verify that data types match expectations for each column.
- Convert data types where necessary for consistency.
Data security and privacy checks:
- Ensure sensitive data is masked or anonymized.
- Verify compliance with data privacy regulations (for example, GDPR, HIPAA).
Data duplication across sources:
- Check for duplication across different data sources or tables.
- Merge duplicate records if needed.
Data time-series validations:
- Validate timestamp ordering for time-series data.
- Detect gaps or overlaps in time intervals.
Schema consistency:
- Ensure schema consistency across data sources or versions.
Record-Level validation:
- Validate data at the individual record level to identify anomalies or discrepancies.
Cross-column relationships:
- Validate logical relationships between columns (for example, age and birthdate).
Data profiling for performance optimization:
- Profile data to identify performance bottlenecks and optimize storage or processing strategies.