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Hybrid and Multi-Cloud Data Architectures
As single-cloud environments face rising security risks and AI workloads strain traditional infrastructure, hybrid and multi-cloud data architecture has become essential. This path teaches you to architect data placement, security, and governance across AWS, Azure, GCP, and on-premises environments. You'll explore data fabric and mesh patterns, design for AI/ML workloads spanning distributed sources, and learn to migrate and operate resilient, cost-effective data platforms built for a multi-cloud world.
**This learning path is actively in production. More content will be added to this path as it gets published and becomes available in the library. To view the full list of content to be included in this Path, click the "About this path" tab below.**
Content in this path
Core Concepts in Hybrid and Multi-Cloud Data Architecture
Every hybrid or multi-cloud data strategy rests on the same three questions: what are we actually building, where does the data live, and how do we keep it secure across boundaries that no longer map to a single vendor's controls. This section answers all three, giving you the conceptual grounding and core skills that apply regardless of which patterns or workloads you go on to build.
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What You'll Learn
- How to architect data placement, movement, and governance decisions across hybrid and multi-cloud environments instead of being locked into a single vendor's ecosystem.
- How to secure data across cloud boundaries and evaluate data fabric and mesh patterns for real-world architecture decisions.
- How to design, migrate, and operate a complete hybrid/multi-cloud data platform, including support for AI/ML workloads that span distributed data sources.
- \--- Core Concepts in Hybrid and Multi-Cloud Data Architecture ---\
- 1. Hybrid and Multi-Cloud Data Architecture: The Big Picture \
- 2. Data Placement, Movement, and Gravity Across Clouds\
- 3. Securing and Governing Data Across Cloud Boundaries \
- \
- -- Applied Architecture and Delivery ---\
- 4. Data Fabric and Data Mesh for Hybrid Environments \
- 5. Multi-Cloud Data Strategies for AI and ML Workloads \
- 6. Designing, Migrating, and Operating Hybrid/Multi-Cloud Data Platforms
- Learners should already be comfortable operating infrastructure within at least one cloud environment day-to-day: provisioning resources, managing basic networking and storage, and understanding core cloud service models (IaaS, PaaS, SaaS). This path doesn't teach single-cloud fundamentals or basic cloud administration from scratch; it builds specialized, cross-environment architecture skills, including data placement and governance, security across cloud boundaries, and hybrid/multi-cloud platform design and migration, on top of that foundation.