> For the complete documentation index, see [llms.txt](https://data-distilller.gitbook.io/adobe-data-distiller-guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution.md).

# Unit 5:  DATA DISTILLER IDENTITY RESOLUTION

- [IDR 100: Identity Graph Overview](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-100-identity-graph-overview.md): In Adobe's Real-Time Customer Profile, an identity graph is a core component that maps various identifiers associated with individual customers across multiple devices, touchpoints, and interactions.
- [IDR 200: Extracting Identity Graph from Profile Attribute Snapshot Data with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-200-extracting-identity-graph-from-profile-attribute-snapshot-data-with-data-distiller.md): An identity lookup table is a database table used to store identities associated with various identity namespaces in the Real-Time Customer Profile.
- [IDR 300: Understanding and Mitigating Profile Collapse in Identity Resolution with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-300-understanding-and-mitigating-profile-collapse-in-identity-resolution-with-data-distiller.md): Mastering profile cleanup transforms data chaos into clarity, enabling accurate, unified real-time customer profiles with 15+ algorithms.
- [IDR 301: Using Levenshtein Distance for Fuzzy Matching in Identity Resolution with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-301-using-levenshtein-distance-for-fuzzy-matching-in-identity-resolution-with-data-distiller.md): Learn how to apply fuzzy matching with Data Distiller to improve accuracy in identity resolution and profile management.
- [IDR 302: Algorithmic Approaches to B2B Contacts - Unifying and Standardizing Across Sales Orgs](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-302-algorithmic-approaches-to-b2b-contacts-unifying-and-standardizing-across-sales-orgs.md): Learn algorithmic techniques for merging, deduplicating, and enriching B2B contact data to create unified, accurate profiles using Data Distiller
- [IDR 302: K-Means Clustering for Identity Resolution with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-302-k-means-clustering-for-identity-resolution-with-data-distiller.md)
- [\[DRAFT\]IDR 300: Probabilistic Identity Resolution Using Fuzzy Matching and Blocking Techniques](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/draft-idr-300-probabilistic-identity-resolution-using-fuzzy-matching-and-blocking-techniques.md)