Enterprise Intelligence with Databricks

Reference Architecture Version 1.0

A Multi-Cloud Implementation of the Arqua Enterprise Intelligence Architecture

Category: Enterprise Implementation Patterns

Status: Reference Architecture Version 1.0

Author: Arqua Pty Ltd

Classification: Public

Last Updated: July 2026

Executive Summary

Modern enterprises rarely operate on a single technology platform.

Business capabilities span multiple cloud providers, enterprise applications, operational technologies and AI services. SAP, Workday, Salesforce, Microsoft Fabric, Amazon Web Services, Snowflake, ServiceNow and custom applications frequently coexist within the same enterprise.

This diversity creates a fundamental architectural challenge.

Enterprise Intelligence cannot depend upon a single platform.

It must be assembled across them.

This paper proposes that Databricks occupies a distinct position within the Arqua Enterprise Intelligence Architecture.

Rather than acting as the enterprise itself, Databricks provides an implementation environment for assembling, governing and operationalising enterprise representations across heterogeneous technology ecosystems.

Databricks therefore becomes an Enterprise Intelligence integration platform rather than a platform replacement.

It enables enterprises to construct Qualified Operational Understanding across multiple clouds while preserving authoritative ownership, semantic integrity and platform independence.

1. Purpose

This paper defines the role of Databricks within a multi-cloud Enterprise Intelligence Architecture.

Unlike cloud-provider-specific implementations, this reference architecture demonstrates how Databricks contributes to Enterprise Intelligence without requiring enterprises to consolidate onto a single technology platform.

The constitutional architecture remains unchanged.

Databricks implements selected architectural responsibilities.

It does not define them.

2. The Multi-Cloud Reality

Most enterprises already operate heterogeneous technology estates.

Typical enterprise platforms include:

• Microsoft Fabric;
• Amazon Web Services;
• Google Cloud;
• SAP;
• Workday;
• Salesforce;
• ServiceNow;
• Snowflake;
• operational technologies; and
• edge systems.

Each platform owns part of the enterprise.

None owns the enterprise itself.

Enterprise Intelligence therefore cannot emerge from any individual platform.

It must emerge from the coordinated assembly of authoritative enterprise representations across them.

3. Constitutional Architecture

The constitutional responsibilities remain unchanged:

• Enterprise Coherence;
• Enterprise Representation Intelligence;
• Institutional Memory;
• No access;
• Operational Assembly Environment;
• Enterprise Coordination;
• Execution Admissibility Architecture; and
• Enterprise Learning.

These responsibilities remain independent of Databricks.

Databricks implements selected capabilities required by these responsibilities.

4. Architectural Principles

DP-1 — Platform Independence

Enterprise Intelligence spans technology platforms.

No platform becomes the enterprise.

DP-2 — Enterprise Products Remain Authoritative

Databricks consumes authoritative enterprise products.

It does not assume ownership of enterprise truth.

DP-3 — Runtime Context is Cross-Platform

Runtime Context Assembly may consume representations originating from multiple technology ecosystems simultaneously.

DP-4 — Governance Remains Distributed

Governance remains aligned to enterprise ownership rather than physical storage.

DP-5 — AI Consumes Qualified Operational Understanding

Artificial Intelligence reasons over qualified operational context assembled across platforms.

DP-6 — Databricks Accelerates Enterprise Intelligence

Databricks provides implementation capabilities that accelerate Enterprise Intelligence without redefining constitutional architecture.

5. Databricks within Enterprise Intelligence

Databricks occupies a distinct architectural position.

It provides capabilities for:

• data engineering;
• streaming;
• transformation;
• orchestration;
• AI;
• analytics;
• product publication;
• federation; and
• operational assembly.

Databricks is therefore neither:

• an enterprise application;
• the Enterprise Registry;
• Institutional Memory;
• enterprise ontology; nor
• operational authority.

Instead, it acts as a governed Operational Assembly and Data Intelligence environment participating within the wider Enterprise Intelligence Architecture.

6. Enterprise Representation

Enterprise representations continue to originate from Business and Data Domains.

Examples include:

• Source-Aligned Data Products;
• SAP;
• Workday;
• Salesforce;
• Microsoft Fabric;
• AWS operational platforms;
• Snowflake;
• IoT platforms;
• enterprise documents;
• telemetry; and
• digital twins.

Databricks consumes these authoritative representations while preserving ownership.

Databricks may transform, project, enrich or assemble representations for a defined operational purpose.

It does not become the authoritative owner of those representations unless the enterprise explicitly assigns that authority through governance.

7. Runtime Context Assembly

Databricks provides a capable environment for Runtime Context Assembly.

Operational Intent determines:

• participating enterprise products;
• required identities;
• governing policies;
• semantic relationships;
• operational authority;
• required freshness;
• permitted use; and
• consequence boundary.

Databricks contributes:

• Lakeflow;
• Delta Live Tables;
• Structured Streaming;
• Lakehouse Federation;
• Databricks SQL;
• Workflows;
• serverless compute; and
• Delta Lake.

These capabilities assemble operational context while preserving authoritative enterprise ownership.

Runtime Context Assembly produces Qualified Operational Understanding.

It does not authorise execution by itself.

8. Enterprise Registry

The Enterprise Registry remains an enterprise architectural capability.

Databricks contributes through:

• Unity Catalog;
• metadata;
• lineage;
• governed assets;
• APIs; and
• discovery services.

Unity Catalog contributes to the Enterprise Registry.

It is not the Enterprise Registry itself.

Enterprise Registry responsibilities extend beyond any individual technology platform and include operational authority, semantic definitions, interface contracts, policies, purpose applicability and consequence boundaries.

9. Institutional Memory

Institutional Memory remains enterprise-owned.

Databricks contributes through:

• Delta Lake;
• Unity Catalog;
• versioned datasets;
• lineage;
• governed data products; and
• historical evidence.

Institutional Memory extends beyond Databricks to include enterprise documents, semantic models, operational evidence, decisions, policies, relationships and outcomes.

Databricks provides memory substrates and evidence support.

It does not own Institutional Memory.

10. Semantic Resolution

Enterprise semantics remain independent of implementation technology.

Databricks contributes:

• governed metadata;
• semantic projections;
• business terminology;
• AI Search; and
• vector search.

Enterprise semantic authority remains implemented through enterprise ontology and knowledge graph services.

Semantic meaning remains enterprise-owned.

Platform metadata may support semantic interpretation.

It does not replace enterprise ontology, semantic governance or institutional authority.

11. Governance

Databricks provides governance capabilities through:

• Unity Catalog;
• lineage;
• auditing;
• access policies;
• data quality;
• governed AI assets; and
• policy-aware data and AI management.

Governance contributes to Enterprise Intelligence but remains broader than platform governance.

Business authority, operational authority and Execution Admissibility remain constitutional responsibilities.

Unity Catalog and Databricks governance services can support policy enforcement, evidence and traceability.

They do not determine enterprise accountability by themselves.

12. Artificial Intelligence

Databricks provides AI implementation capabilities.

These include:

• Mosaic AI;
• Mosaic AI Agent Framework;
• Foundation Model APIs;
• MLflow;
• Model Serving;
• AI Functions;
• AI Search; and
• Agent Evaluation.

Within Enterprise Intelligence these capabilities:

• consume Qualified Operational Understanding;
• support reasoning;
• perform prediction;
• execute specialised AI;
• assist Business Domains;
• support agentic interaction; and
• provide model and agent lifecycle evidence.

They do not independently construct enterprise truth.

AI services must remain bound to accepted representations, governed context, semantic authority, provenance and human accountability.

13. Multi-Cloud Operational Assembly

One of Databricks’ strengths is its ability to assemble enterprise representations across technology ecosystems.

Typical Runtime Context Assembly may include:

• SAP financial products;
• Workday workforce products;
• Microsoft Fabric operational products;
• AWS operational events;
• Snowflake analytical products;
• enterprise knowledge graph;
• streaming telemetry; and
• business policies.

Databricks assembles these into Qualified Operational Understanding while preserving enterprise ownership across all participating platforms.

The value is not consolidation.

The value is governed cross-platform assembly.

14. Multi-Cloud Enterprise Intelligence

Enterprise Intelligence extends across cloud providers.

No cloud provider becomes the enterprise.

Each contributes to Enterprise Intelligence according to its architectural responsibilities.

15. Databricks Capability Mapping

16. Architecture Decision Records

ADR-001 — Databricks participates without becoming the enterprise

Databricks shall implement Enterprise Intelligence without becoming the enterprise.

ADR-002 — Unity Catalog contributes to the Enterprise Registry

Unity Catalog contributes to the Enterprise Registry but does not replace it.

ADR-003 — Semantic authority remains independent

Enterprise semantic authority remains independent of platform metadata.

ADR-004 — Operational Assembly preserves Business Domain ownership

Operational Assembly shall preserve Business Domain ownership of enterprise products.

ADR-005 — AI consumes Qualified Operational Understanding

AI services shall consume Qualified Operational Understanding rather than independently constructing enterprise truth.

ADR-006 — Enterprise Intelligence remains portable

Enterprise Intelligence shall remain portable across cloud providers.

17. Non-Goals

This architecture does not:

• require enterprise consolidation onto Databricks;
• replace Microsoft Fabric;
• replace AWS;
• replace Snowflake;
• replace enterprise applications;
• replace enterprise ontology;
• replace Business Domain ownership;
• centralise enterprise knowledge within Databricks;
• make Unity Catalog the Enterprise Registry;
• make Delta Lake Institutional Memory;
• make Mosaic AI the intelligence architecture; or
• redefine the Arqua Enterprise Intelligence Architecture.

Databricks participates within Enterprise Intelligence.

It does not replace it.

18. Reference Architecture

19. Implementation Roadmap

Phase 1 — Enterprise Foundations

Establish Enterprise Registry, enterprise ontology and Source-Aligned Data Products.

Phase 2 — Cross-Platform Connectivity

Connect authoritative products from Microsoft, AWS, SAP, Snowflake and operational systems.

Phase 3 — Operational Assembly

Implement Runtime Context Assembly, Operational Context Manifests and Context Qualification using Databricks capabilities.

Phase 4 — Enterprise AI

Introduce Mosaic AI and governed agent capabilities that consume Qualified Operational Understanding.

Phase 5 — Enterprise Coordination

Enable Business Domains, operational applications and AI agents to coordinate execution across multiple cloud environments.

Phase 6 — Enterprise Learning

Continuously preserve operational outcomes within Institutional Memory while refining Runtime Context Assembly rules and qualification criteria.

20. Architecture of Record

A Databricks implementation should be recorded in an Architecture of Record.

The Architecture of Record should identify:

• which Databricks capabilities implement which architectural responsibilities;
• which enterprise products remain authoritative;
• which platforms participate in cross-platform assembly;
• which registry sources participate;
• which semantic services are authoritative;
• which context is assembled at runtime;
• which identities and policies apply;
• which workflows can execute;
• which AI and agent services participate;
• which actions require admissibility evaluation;
• which evidence is captured; and
• which outcomes feed Enterprise Learning.

The Architecture of Record ensures that multi-cloud implementation does not become informal integration.

21. Boundary Conditions

Databricks should not be described as:

• the enterprise itself;
• the owner of enterprise knowledge;
• the source of enterprise truth;
• the definition of Institutional Memory;
• the Enterprise Registry itself;
• the enterprise ontology;
• the substitute for governance;
• the decision-maker for consequence-bearing action;
• the owner of enterprise semantics; or
• the constitutional architecture itself.

Databricks may implement capabilities that support these responsibilities when governed by the Arqua Enterprise Intelligence Architecture.

Reference Architecture Diagrams

Conclusion

Databricks occupies a distinct position within modern Enterprise Intelligence Architecture.

Unlike cloud-provider platforms, it naturally operates across heterogeneous technology ecosystems.

This enables Databricks to become an implementation environment for Operational Assembly, AI, analytics and governed data products without requiring enterprises to abandon existing investments.

Within the Arqua Enterprise Intelligence Architecture, Databricks does not replace Microsoft Fabric, AWS, Snowflake or enterprise applications.

Instead, it enables these platforms to participate in a coherent Enterprise Intelligence capability founded upon authoritative enterprise representations, Runtime Context Assembly and Qualified Operational Understanding.

This architecture demonstrates that Enterprise Intelligence is not achieved through platform consolidation.

It is achieved through constitutional architecture realised consistently across multiple technology ecosystems.

Related Architecture Papers

• Enterprise Intelligence Framework
• No access
• Operational Assembly Environment
• Enterprise Intelligence on the Microsoft Platform
• Enterprise Intelligence on AWS
• Enterprise Representation Intelligence
• Institutional Memory
• System Model Foundation
• Enterprise Coordination
• Execution Admissibility Architecture

Version History

Document Status: Public Reference Architecture

Publication Date: 2026

Version: 1.0

Last Updated: July 2026

Owner: Arqua Pty Ltd

Author: Arqua Pty Ltd

Portfolio: Enterprise Intelligence Architecture

SEO Page Title: Enterprise Intelligence with Databricks | Arqua

SEO Meta Description: Platform Realisation describing how Databricks participates in Enterprise Intelligence through multi-cloud Runtime Context Assembly, Operational Assembly, Qualified Operational Understanding, Business Domains and enterprise architecture without defining the architecture itself.

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