databrickslabs / databrickslabs/dqx
[FEATURE]: Fuzzy matching check
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 459
- Forks
- 147
- Avg merge
- 2d 13h
- Merged PRs (30d)
- 9
Description
Is there an existing issue for this?
- I have searched the existing issues
Problem statement
Product matching plays a critical role in healthcare, retail, and consumer goods organizations, where accurate identification of similar or duplicate items is essential for operations and analytics.
Proposed Solution
Implement product matching as a standalone check.
Integrate it into the compare_datasets module to enhance cross-dataset validation. Extend the forgein_key check to support flexible matching where exact equality is not guaranteed.
https://www.databricks.com/blog/using-images-and-metadata-product-fuzzy-matching-zingg
Use vector search libraries & algorithm, e.g. Approximate Nearest Neighbor (ANN).
Additional Context
No response
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating the compare_datasets module and the existing forgein_key check. Review how a standalone product-matching check could use vector search or an ANN algorithm for flexible cross-dataset matching. Done means the new check is implemented and integrated into compare_datasets with a defined matching behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, spark
- Domain
- data-engineering
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100