exact deduplication

**Exact deduplication** is the **removal of records that are byte-identical or normalized-text identical within a dataset** - it is the fastest first-pass step in data cleaning pipelines. **What Is Exact deduplication?** - **Definition**: Uses hashing of normalized text to detect exact repeated entries. - **Pipeline Position**: Usually applied before more expensive fuzzy deduplication stages. - **Normalization**: Whitespace, casing, and markup normalization can increase exact-match coverage. - **Limit**: Cannot capture semantically similar but non-identical duplicates. **Why Exact deduplication Matters** - **Efficiency**: Removes low-value redundancy with minimal compute overhead. - **Compute Savings**: Prevents repeated training on identical content. - **Pipeline Hygiene**: Improves quality baseline before approximate matching. - **Traceability**: Hash-based records simplify auditing and reproducibility. - **Foundation**: Essential prerequisite for robust multi-stage dedup workflows. **How It Is Used in Practice** - **Canonicalization**: Define consistent normalization rules before hashing. - **Hash Strategy**: Use collision-resistant hashes with scalable indexing. - **Incremental Runs**: Apply exact dedup at each ingestion stage to control growth. Exact deduplication is **a foundational low-cost dedup stage in data-preparation pipelines** - exact deduplication should be automated and repeatable to maintain corpus quality at scale.