Duration

2 Days

Level

Architect

Prerequisites

Understanding of basic data concepts

Format

Onsite, Virtual, Hybrid

Certificate

Certificate of Participation

Audience

Architects, Leads, Senior Professionals

Who Should Attend

This training is ideal for:

Data Architects
Enterprise Architects
Solution Architects
Data Engineering Leads
Data Managers
Analytics Leaders
Technology Leaders
Digital Transformation Teams
Governance Professionals
Senior Data Practitioners

Why Enterprise Data Architecture Matters

Many organizations invest heavily in cloud platforms, data lakes, analytics tools, and AI solutions.

Yet many initiatives fail because the architecture supporting them was never properly designed.

Common challenges include:

  • Data silos
  • Duplicate data
  • Poor governance
  • Inconsistent reporting
  • Integration complexity
  • Limited scalability
  • Difficult AI adoption

Enterprise Data Architecture provides the blueprint that connects business strategy, information assets, applications, governance, and technology.

A strong architecture creates consistency, trust, scalability, and business value — the foundation of effective Data Ecosystem Design and Modern Data Architecture.

Learning Outcomes

After completing this training, participants will be able to:

  • Understand the role of Enterprise Data Architecture
  • Align business objectives with data strategy
  • Differentiate architecture domains and responsibilities
  • Design modern enterprise data ecosystems
  • Understand Data Warehouse, Data Lake, and Lakehouse architectures
  • Evaluate Data Hub and Data Fabric concepts
  • Understand Data Mesh and Federated approaches
  • Design architecture layers for scalable data platforms
  • Improve governance and data quality outcomes
  • Prepare organizations for analytics and AI initiatives

Course Structure

1

Introduction to Enterprise Architecture

Understanding architecture as a business capability.

2

Architecture Domains

Business, Data, Application, and Technology Architecture — and how the four domains work together.

3

Modern Data Ecosystems

How information flows through modern organizations. Enterprise data lifecycle concepts.

4

Data Architecture Layers

Landing Zone, Staging Zone, Preparation Zone, and Semantic Zone — purpose and responsibilities of each layer.

5

Modern Data Platform Architectures

Data Warehouse, Data Lake, and Data Lakehouse — benefits, challenges, and use cases.

6

Data Integration Architectures

Data Hub, Virtualization, and API-Based Integration — modern integration approaches.

7

Advanced Enterprise Architectures

Data Fabric, Data Mesh, and Federated Data Architecture — domain ownership and governance models.

8

Governance and Trust

Data Governance, Metadata, Security, Privacy, and Data Quality — architecture considerations for trust and compliance.

9

AI-Ready Architectures

Feature Stores, Vector Databases, and Inference Layers — modern architectural considerations for AI and Agentic AI.

10

Data & AI Cognitive (DAC) Architecture

Overview of the DAC Architecture framework and how it brings together modern data and AI capabilities within a unified enterprise architecture.

Connecting Business and Technology

These domains work together to deliver business value through cohesive enterprise design.

Business Architecture
Data Architecture
Application Architecture
Technology Architecture

Evolution of Enterprise Data Platforms

Each architectural evolution responds to changing business drivers — from structured reporting to federated, AI-ready data ecosystems.

Data Warehouse Data Lake Data Lakehouse Data Hub Data Fabric Federated Data Ecosystems

Beyond Technology Platforms

Most architecture courses focus on specific technologies or cloud vendors. This training focuses on architectural thinking. Participants learn:

Why architectures exist
How architectures evolve
How architecture supports business outcomes
How governance is embedded into architecture
How organizations prepare for AI at scale

The emphasis is on principles, patterns, and enterprise design rather than specific tools.

Industry Architecture Scenarios

Examples from:

Banking Telecommunications Healthcare Retail Oil & Gas Government

Demonstrate:

Governance Challenges

Platform Evolution

Integration Patterns

Analytics Requirements

AI Readiness

Why Learn From YPT

Enterprise Architecture Experience

Practical experience designing enterprise-scale data ecosystems.

Published Author

Author of books covering Data Architecture, Data Management, and AI.

Global Data Community

43,000+ members.

International Podcast

Host of the "Let's Talk About Data!" podcast.

Architecture Frameworks

Creator of DAC Architecture and other enterprise frameworks for unified data and AI capability design.

Organizational Benefits

Organizations that invest in Enterprise Data Architecture can:

Reduce data silos
Improve governance effectiveness
Accelerate analytics initiatives
Improve platform scalability
Increase AI readiness
Strengthen business and technology alignment
Support long-term digital transformation

Related Training Programs

Design Future-Ready Data Ecosystems

Learn how modern organizations design scalable, governed, and AI-ready enterprise data architectures that support long-term business success.

Part of the YPT Data & AI Training portfolio. Build modelling foundations with Enterprise Data Modelling.