Modern Data and Integration Stack: Enterprise Architecture Taxonomy

I. Cloud Platform Stack

A. Microsoft stack

• Azure as core cloud operating platform.
• Azure Synapse Analytics for analytical warehousing and orchestration.
• Azure Data Factory for ETL and pipeline control.
• Azure SQL and Cosmos DB for transactional and globally distributed persistence.

B. AWS stack

• S3 data lake foundations.
• Redshift warehouse layer.
• AWS Glue for managed ETL.

C. Google Cloud stack

• BigQuery for serverless analytics.
• Dataflow for stream and batch pipeline orchestration.

Related hubs: AWS, Google Cloud, Cloud Infrastructure, Enterprise Technology Services.

II. Data Engineering, MDM, and Governance

A. Modern data stack

• Databricks for lakehouse compute and model-ready pipelines.
• Snowflake for elastic cloud warehousing and governed data sharing.
• Apache Spark for distributed transformation and machine learning preparation.

B. Master data management

• IBM InfoSphere and Oracle MDM for canonical entity governance.
• Cross-domain harmonization for customer, product, supplier, and chart-of-accounts entities.

C. Data governance

• Collibra and Alation for stewardship workflows, glossary control, and lineage awareness.

Related hubs: Data Integration, BI and Analytics, AI Platforms, AI Automation.

III. Identity and Security Control Layer

A. Authentication and authorization

• SAML SSO for enterprise federation models.
• OAuth 2.0 and OpenID Connect for delegated and API-centric authorization.

B. Identity providers

• Azure AD (Entra ID) for Microsoft-centric enterprise identity fabrics.
• Okta and Ping Identity for multi-platform IAM and policy orchestration.

Identity design quality is a major predictor of integration reliability, application onboarding speed, and compliance assurance across ERP and data ecosystems.

Related hubs: Cybersecurity, Integration and API, Digital Transformation.

IV. Integration, Middleware, and Event Fabric

iPaaS and middleware

• MuleSoft for API-led integration and Salesforce-centric landscapes.
• Dell Boomi for broad connector ecosystems.
• Celigo for NetSuite-native and ERP-focused accelerators.

API management

• Kong and Apigee for policy enforcement, rate control, and lifecycle governance.

Event streaming

• Apache Kafka and Azure Event Hub for event-driven enterprise orchestration.

Related hubs: NetSuite, Infor Lawson, Oracle, SAP, Salesforce.

V. Enterprise Data Architecture Patterns

A. ETL and ELT pipeline models

• Batch plus real-time ingestion architecture.
• CDC-enabled data synchronization for low-latency analytics and operational intelligence.

B. Lakehouse architecture

• Convergence of lake flexibility and warehouse reliability.
• Strong fit for AI-ready, multi-workload data operating models.

C. Data mesh (emerging)

• Domain-oriented data ownership and decentralized governance patterns.
• Requires strong platform abstractions and policy automation to scale safely.

Related hubs: Cross-System Process Framework, Enterprise ERP Taxonomy, Implementation Methodology.

VI. Wave 4 Integration Fabric Synthesis

Integration synthesis launch: Integration Fabric Enterprise Architecture extends stack components into an enterprise control model for API, event, iPaaS, and identity policy orchestration.

Frequently Asked Questions: Modern Data and Integration Architecture

What is the modern enterprise data and integration stack?

It is a layered architecture combining cloud platforms, lakehouse and warehouse services, IAM controls, API and event middleware, and governed data engineering patterns.

How do Databricks and Snowflake differ in architecture roles?

Databricks is typically used for lakehouse compute and model-ready pipelines, while Snowflake is often used for elastic warehousing, governed sharing, and analytics delivery.

Why are IAM standards like SAML, OAuth, and OIDC included in data architecture?

Identity standards define trust boundaries for users, applications, and APIs, which directly impacts integration security, onboarding speed, and policy compliance.

When should organizations use iPaaS and event streaming together?

Use both when process orchestration requires transactional API flows plus asynchronous event propagation across ERP, CRM, analytics, and operational systems.