> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wazoo.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Google Dataplex

> Worlds vs Google Dataplex: RDF fact ledger vs enterprise data fabric & governance.

## At a glance

|               | Worlds                                                | [Google Dataplex](https://cloud.google.com/dataplex)      |
| :------------ | :---------------------------------------------------- | :-------------------------------------------------------- |
| Primary role  | Authoritative RDF fact ledger & SPARQL context engine | Enterprise data fabric, governance, & metadata management |
| Data model    | RDF triples/quads in an append-only graph ledger      | Distributed data lakes, BigQuery tables, & asset catalog  |
| Retrieval     | SPARQL graph queries, full-text, & vector fusion      | Data catalog search, SQL queries, & policy controls       |
| Core strength | Deterministic multi-hop SPARQL graph verification     | Cross-cloud data governance, quality, & lineage metadata  |
| Deployment    | Edge-ready adapters (LibSQL, Postgres) or Cloud       | Google Cloud Platform (GCP)                               |

## Overlap and shared capabilities

Both Worlds and [Google Dataplex](https://cloud.google.com/dataplex) provide
structure, lineage tracking, and governance over enterprise data assets so
applications and AI models can consume reliable information.

The difference is structural and operational:

* Google Dataplex is an enterprise data fabric platform within GCP. It unifies
  metadata management, data quality checks, security policies, and cataloging
  across distributed BigQuery datasets and GCS data lakes.
* Worlds is an RDF knowledge graph context engine. It manages persistent triple
  assertions, subject-predicate-object graphs, and SPARQL queries with immutable
  quad provenance for AI agents.

Dataplex manages enterprise data lakes and governance policies across Google
Cloud, while Worlds provides a queryable, verifiable RDF fact ledger for
software agents.

## When Worlds fits

* You need deterministic graph querying (SPARQL) to verify multi-hop
  relationships across domain facts.
* You require immutable provenance and quad-level assertion tracking for AI
  agent context.
* You want open-standard RDF schemas that operate natively across edge adapters
  (LibSQL, Postgres) and cloud infrastructure.

## When Google Dataplex fits

* You manage enterprise data lakes and BigQuery warehouses requiring centralized
  governance, data quality monitoring, and access controls on GCP.
* You need automated data discovery, classification, and business glossary
  cataloging across Google Cloud assets.
* Your primary data infrastructure relies on GCP analytical storage rather than
  RDF graph stores.

## Coexistence and integration

Google Dataplex and Worlds operate at different levels of the enterprise data
architecture. Dataplex governs large-scale GCP data lakes and relational
analytical tables, while Worlds acts as the context engine and RDF fact ledger
for AI agent fleets.