> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/zingg/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/zingg/zingg-concepts/zingg-entity-resolution-platform.md). # Zingg Entity Resolution Platform Zingg is an ML-powered entity resolution engine built to run where your data already lives directly on your warehouse or lakehouse, with no data movement and no rules to write or maintain. Three capabilities work together to handle the full problem:

Warehouse-native execution Probabilistic + deterministic matching Persistent identity graph

Warehouse-native execution	Probabilistic + deterministic matching	Persistent identity graph
Zingg runs inside Databricks, Microsoft Fabric, Snowflake, GCP Dataproc, AWS Glue, and AWS EMR. Your data never leaves your environment. No ETL pipelines. No external APIs. There is no separate infrastructure to operate. The same model that runs on 100,000 records scales to hundreds of millions using your existing Spark or Snowflake compute, without any architectural changes.	Probabilistic matching is Zingg's default and available in the Community Edition. The ML model learns from your labeled pairs; 30 to 50 examples are enough to build a model calibrated to your specific data and scores every candidate pair on multiple field-level features. It handles typos, abbreviations, missing values, and format variations automatically. Deterministic matching (Enterprise) adds hard rules for trusted identifiers. When two records share the same SSN, tax ID, or email, Zingg treats them as the same entity without consulting the ML model. Both approaches run in a single flow, finding all possible matches. → Deterministic vs Probabilistic Matching	Every resolved entity receives a `Zingg ID`, a globally unique, persistent GUID assigned in Enterprise that remains stable across runs, incremental updates, and model changes. Community produces a `Z Cluster` which is unique. Matching records share the same `Z_Cluster` that is reassigned each run. Enterprise produces a `Zingg ID` you can store in downstream systems with confidence. The identity graph grows incrementally. New records are matched to existing clusters without rerunning on your full dataset. → Identity Graph → Z Cluster and Zingg ID

Zingg runs inside Databricks, Microsoft
Fabric, Snowflake, GCP Dataproc, AWS Glue, and AWS EMR. Your data never
leaves your environment. No ETL pipelines. No external APIs. There is no separate infrastructure to operate. The same model that runs on 100,000 records scales to hundreds of millions using your existing Spark or Snowflake compute, without any architectural changes.

Probabilistic matching is Zingg's default and available in the Community Edition. The ML model learns from your labeled pairs; 30 to 50 examples are enough to build a model calibrated
to your specific data and scores every candidate pair on multiple field-level features. It handles
typos, abbreviations, missing values, and format variations automatically.

Deterministic matching (Enterprise) adds hard rules for trusted identifiers.
When two records share the same SSN, tax ID, or email, Zingg treats them
as the same entity without consulting the ML model.

Both approaches run in a single flow, finding all possible matches.

→ Deterministic vs Probabilistic
Matching

Every resolved entity receives a Zingg ID, a globally unique, persistent GUID assigned in Enterprise that remains stable
across runs, incremental updates, and model changes.

Community produces a Z Cluster
which is unique. Matching records share the same Z_Cluster that is reassigned each run.

Enterprise produces a Zingg ID you can store in downstream systems
with confidence.

The identity graph grows incrementally. New records are matched to existing clusters without rerunning on your full dataset.

→ Identity Graph
→ Z Cluster and Zingg ID

This is the combination that makes enterprise-scale entity resolution computationally feasible and practically maintainable. {% hint style="success" icon="right-long" %} **Read more**: * [Deterministic vs Probabilistic Matching](/zingg/zingg-concepts/zingg-entity-resolution-platform/deterministic-vs-probabilistic-matching.md) * [How Zingg Learns](/zingg/zingg-concepts/how-zingg-learns.md) * [Zingg Models](/zingg/zingg-concepts/how-zingg-learns/zingg-models.md) (blocking + similarity) {% endhint %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.zingg.ai/zingg/zingg-concepts/zingg-entity-resolution-platform.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.