Metadata Manipulation
Metadata manipulation refers to the process of creating, updating, or modifying dataset metadata to ensure it remains accurate, relevant, and useful. This is especially important when datasets evolve over time or are used in different contexts.
Common Metadata Manipulation Tasks
- Adding Metadata
- Add new metadata fields to describe additional features or contextual information.
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Example: Adding a new feature description after a dataset update.
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Updating Metadata
- Modify existing metadata to reflect changes in the dataset (e.g., new data types, updated descriptions).
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Example: Updating the
data_type
to match the correct type of column. -
Removing Metadata
- Delete outdated or irrelevant metadata fields.
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Example: Removing metadata for a feature that has been dropped from the dataset.
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Validating Metadata
- Ensure metadata is consistent with the dataset's actual structure and content.
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Example: Checking that all features listed in the metadata exist in the dataset.
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Exporting Metadata
- Save metadata in a standardized format (e.g., JSON, YAML) for sharing or documentation purposes.
- Example: Exporting metadata to a YAML file for inclusion in a data catalog.