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Enterprise Architecture Decisions & Future-Ready Design 2026
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 31m | Size: 6.5 GB
Master architecture trade-offs, decision frameworks, AI governance, data products, observability, and future-ready enter
What you'll learn
Evaluate enterprise architecture trade-offs and make practical, defendable decisions.
Apply decision frameworks when requirements, constraints, and information are incomplete.
Identify architecture anti-patterns and avoid overengineering in enterprise solutions.
Compare build vs buy vs managed services and evaluate vendor lock-in strategically.
Design future-ready architectures using data products, contracts, observability, metadata, and knowledge layers.
Apply AI governance, Agentic AI security, LLMOps, AgentOps, privacy, and compliance principles.
Design self-service data platforms using platform engineering and governed delivery patterns.
Communicate architecture recommendations clearly to executives and stakeholders.
Requirements
Basic understanding of data, BI, cloud, or enterprise systems is helpful but not mandatory.
No programming experience is required.
No specific cloud platform or BI tool is required.
Prior exposure to solution architecture, data architecture, BI architecture, or cloud architecture will help learners get more value from the course.
Learners should have an interest in moving toward senior architect or enterprise architect responsibilities.
Description
Phase 5 brings the entire Enterprise Architecture Series together.
Enterprise architecture is not just about choosing technologies. It is about making the right decisions when business priorities, requirements, budgets, risks, and technologies are constantly changing.
In this course, you will learn how enterprise architects evaluate trade-offs, work with incomplete information, avoid common anti-patterns, balance practical design with overengineering, and decide between build, buy, and managed-service options.
You will also learn how to evaluate vendor lock-in, document architecture decisions, communicate effectively with executives, and develop the broader skills required to become an enterprise architect.
The second half of the course focuses on the next generation of enterprise architecture for 2026-2027. You will explore data products, data contracts, data observability, active metadata, catalogs, lineage, knowledge layers, AI governance, agentic AI security, LLMOps, AgentOps, privacy, data sovereignty, clean rooms, partner analytics, self-service data platforms, and platform engineering.
Finally, we bring everything together through a complete enterprise architecture master scenario and a future-ready architecture blueprint.
By the end of this course, you will be able to evaluate complex architecture scenarios, communicate your decisions clearly, and design enterprise platforms that are practical, governed, scalable, AI-ready, and aligned with business goals
This course used AI to create some of the content
Who this course is for
Solution Architects who want to grow into Enterprise Architect roles.
Data Architects, BI Architects, Cloud Architects, and AI Architects who want a broader enterprise perspective.
Technical Leads, Senior Developers, and Engineers involved in architecture decisions.
Consultants who need to design and communicate enterprise-scale solutions to clients.
Data and BI professionals moving toward architecture and leadership roles.
Architects who want to understand modern topics such as data products, observability, Agentic AI, AI governance, and platform engineering.
Technology leaders who need to evaluate architecture choices based on business value, risk, cost, and long-term sustainability.
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