Deduplication

As explained in Delivering Machine Learning Projects: From design to deployment

Deduplication is the process of identifying and removing redundant records that represent the same underlying entity or event.

Deduplication identifies and removes redundant records representing the same underlying entity or event, preventing inflated aggregates and misleading analysis. section 6.6.2

Treat deduplication as an EDA backlog task alongside join validation and normalization. Verify the result against plausible aggregates and published business figures before using the data for projections. section 6.6.2

Implementation checkpoints:

• Compare aggregates produced with and without deduplication to expose distortion. section 6.6.2
• Combine deduplication with a properly designed normalization process. section 6.6.2
• Cross-check corrected totals against published revenue and document material deltas in the EDA report. section 6.6.2

Figure 6.7 Naïve normalization and deduplication sales data

Compare naïve normalization and deduplication with the resulting aggregate sales picture. section 6.6.2

Figure 6.8 The same data treated for duplicates with a well-designed currency normalization process

Inspect how duplicate treatment combined with currency normalization produces more plausible results. section 6.6.2

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes

Demonstrates validation through aggregate comparisons and published-revenue cross-checks, but does not specify the matching algorithm.

Examples

Contrasts naïve and corrected sales aggregates, then checks corrected totals against published revenue figures.

What you can do after

Diagnose aggregate distortion by comparing duplicate-handling treatments and documenting reconciliation results in an EDA report.

How it connects

Connects deduplication with exploratory data analysis, data normalization, joins, data integrity, and revenue reconciliation.

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Also explained in

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  The Well-Grounded Data AnalystDeduplication is the process of identifying and handling multiple records that represent the same underlying entity.
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  Fabulous Adventures in Data Structures and AlgorithmsDeduplication is the elimination of duplicate representations by sharing one representation for identical data.
• 

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  Architecting an Apache Iceberg LakehouseDeduplication is the removal or suppression of repeated records or events representing the same logical occurrence.
• 

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  Generative AI in ActionDeduplication is the process of identifying and removing duplicate data records or content.

Compare how 5 books explain deduplication →

Related concepts

• entity resolutionused by · Entity resolution is the process of determining which records or mentions refer to the same real-world entity despite differences in their representations.
• exactly-once deliveryaddresses · Exactly-once delivery is a messaging or processing guarantee that each message or input produces its intended effect once, without loss or duplicate effects, despite retries or failures.
• spamaddresses · Spam is unsolicited or unwanted electronic messaging or content, often distributed in bulk and commonly commercial, deceptive, irrelevant, or malicious.
• filteringpart of · Filtering is the operation of selecting or excluding items from data or a collection according to specified criteria.
• training datasetuses · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.

This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.