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cloud.google.com·6h ago
Data Science
Towards Data Science·Sep 2·5 min read
Avoiding Entity Key Drift in a Data Lake: Step 2, When Fuzzy Matching Stops Working
TL;DR
A matcher was developed to address data normalization issues in a data lake, but testing revealed it was unsafe for use. Consequently, the architecture was revised without the matcher.
✦ Why It Matters
Evaluate your data normalization processes to ensure they are robust against entity key drift.
Key Takeaways
- Use strict normalization to merge only identical identifiers.
- Avoid relying solely on fuzzy matching for product identifiers.
- Incorporate human verification against manufacturer catalogs.
- Recognize that identity is not contained within the string itself.
How It Works
The proposed method involves a two-stage matcher that first checks for an exact match of the numeric core of identifiers before assessing the alphabetic portion for similarity. This structure aims to prevent merging distinct products while still catching typos.
⚠ The Catch
Fuzzy matching fails to distinguish between typos and distinct products, leading to potential inaccuracies in data reconciliation.