Enterprise Data Architecture Foundation
Overview
Enterprise Data Architecture Foundation is a comprehensive course designed to equip data professionals with the foundational knowledge and practical skills needed to architect modern enterprise data ecosystems. As organizations increasingly rely on data-driven insights and AI capabilities, robust data architecture has become critical for enabling scalable, secure, and efficient data platforms.
Data is a foundational enterprise asset, driving analytics, automation, and AI initiatives across industries. However, many organizations struggle with fragmented data landscapes, technical debt, and governance challenges that hinder their ability to leverage data effectively. This course addresses these challenges by providing a structured approach to designing and implementing enterprise data architectures that support diverse workloads and evolving business needs.
Participants will explore architectural frameworks, data platform components, cloud-native data infrastructure, and best practices for building scalable and secure enterprise data ecosystems. Through guided labs and design exercises, learners will develop architectural skills that support analytics, operational data workloads, and emerging AI/ML initiatives.
Day 1 focuses on foundational data architecture concepts and platform design. Day 2 emphasizes governance, security, operationalization, and strategic data architecture planning.
Duration
2 Days
Who Should Take This Course
Audience
Data Architects, Ops & Engineers, Cloud Engineers, Analytics Engineers, Technical Managers.
Prerequisites
Participants should have:
- Basic understanding of databases, analytics, or data engineering concepts
- Familiarity with cloud or distributed systems helpful
- General DevOps or platform engineering exposure beneficial
- No prior enterprise data architecture experience required
Why You Should Take This Course
Upon completing this course, participants will be able to:
- Understand the principles and scope of enterprise data architecture
- Design modern data platforms supporting analytics, operational, and AI workloads
- Apply data governance, security, and compliance frameworks effectively
- Architect scalable data pipelines and storage solutions
- Integrate cloud-native and hybrid data infrastructure
- Enable data discoverability, quality, and lifecycle management
- Align data architecture strategies with business and operational goals
Course Outline
Enterprise Data Architecture Foundation
Day 1 — Data Architecture Foundations and Platform Design
1. Enterprise Data Architecture Fundamentals
- Role of data architecture in modern enterprises
- Data platform evolution (warehouses, lakes, lakehouses)
- Data lifecycle and data product thinking
- Architectural frameworks and reference models
- LAB: Assessing Enterprise Data Architecture Needs
2. Modern Data Platform Components
- Storage architectures (data warehouses, lakes, lakehouses)
- Data ingestion and integration pipelines
- Batch vs streaming data architectures
- Metadata, catalogs, and data discovery
- LAB: Designing a Data Platform Reference Architecture
3. Data Modeling and Integration
- Conceptual, logical, and physical data models
- Schema design strategies
- Data interoperability and integration patterns
- API-driven data access architectures
- LAB: Data Modeling Exercise
4. Cloud and Hybrid Data Infrastructure
- Cloud-native data platforms
- Hybrid and multi-cloud data strategies
- Data virtualization and federation
- Infrastructure automation considerations
- LAB: Designing a Hybrid Data Platform
Day 2 — Governance, Security, and Operational Excellence
5. Data Governance and Quality
- Governance frameworks and operating models
- Data stewardship and ownership models
- Data quality metrics and validation pipelines
- Data lineage and lifecycle management
- LAB: Designing a Data Governance Framework
6. Data Security and Compliance Architecture
- Data classification and protection strategies
- Identity, encryption, and access control
- Privacy regulations and compliance considerations
- Secure data sharing patterns
- LAB: Designing Secure Data Access Controls
7. Data Operations and Observability
- DataOps principles and automation
- Monitoring data pipelines and data reliability
- Performance and scalability considerations
- Cost optimization and operational efficiency
- LAB: Data Pipeline Observability Exercise
8. Strategic Data Architecture and Future Trends
- Aligning data architecture with business outcomes
- AI/ML data platform considerations
- Data mesh and domain-oriented architectures
- Emerging trends in enterprise data ecosystems
- LAB: Enterprise Data Architecture Capstone Design