• views: 91
• date: 7 April 2026
• posted by: voska89

Master core data modeling paradigms using first-principles frameworks that turn business

Requirements

into defensible architectural decisions.
Build lakehouses, data mesh platforms, streaming pipelines, and serving layers with clear tradeoff reasoning across cloud architectures and open table formats.
Design AI-ready architectures for RAG, vector databases, and agentic AI, with data quality, FinOps cost models, and operational SLOs built in from day one.
Apply governance, security, and compliance by design; prevent costly anti-patterns; and produce a portfolio ADR demonstrating senior architect-level thinking.
Understand how team topology and Conway's Law shape data platforms, and make organizational design decisions that prevent architecture from drifting into chaos.
Evaluate emerging paradigms and build migration-ready architectures that adapt to change without full rewrites — a critical skill for long-lived platforms.

Requirements

Some experience in data engineering, analytics engineering, or a related data platform role.
Comfortable reading and understanding technical concepts — you work with data systems regularly, even if not as a hands-on coder.
Basic familiarity with data pipeline and storage concepts — terms like ETL, data warehouse, or data lake should not be new to you.
No prior architecture experience needed — this course builds structured architectural thinking from the ground up.

Description

Most data engineers can build pipelines. Few can architect systems that survive scale, regulation, cost pressure, and the next paradigm shift. This course closes that gap.Data Architecture Mastery teaches you to think like a senior data architect — not by memorizing tools, but by building a repeatable decision framework you can apply to any stack, any workload, and any constraint.

What you'll learn

:Decompose any business requirement into six architectural constraint dimensions and use a weighted scoring framework to make defensible technology decisionsEvaluate storage paradigms — data warehouse, data lake, lakehouse, data mesh, and data fabric — and choose the right one for your workloadNavigate modern data paradigms: evaluate lakehouse architecture, open table formats, real-time OLAP engines, columnar storage, and medallion architecture — and know when each is the right call versus a legacy warehouseArchitect pipeline patterns across batch ETL/ELT, streaming, CDC, Lambda, Kappa, and Zero ETLApply data modeling paradigms — Kimball star schema, Inmon 3NF, Data Vault 2.0, MDM, SCD and One Big Table — to the right use caseBuild AI-ready platforms with RAG pipelines, embedding architectures, feature stores, and agentic AI systemsDesign the serving layer: semantic layers, data contracts, materialized views, and reverse ETLModel total cost of ownership, apply hot/warm/cold storage tiering, and use FinOps practices to prevent runaway cloud spendDefine SLOs with error budgets, design for failure with chaos engineering, and build production-grade observability into your pipelinesImplement privacy by design, data classification, active metadata, policy-as-code, access control architectures, and GDPR/CCPA compliance patternsApply Strangler Fig, Blue-Green deployment, and Parallel Run patterns for zero-risk platform migrationsAlign architecture with team structure using Conway's Law, and avoid the most costly anti-patterns seen in production systemsDocument every decision using the Architecture Decision Canvas — a portfolio artifact you keep and build on after the courseEvery module builds on a single framework. By the end you'll have a completed Architecture Decision Canvas for a real workload from your own platform — something you can present to leadership, defend in review, and update as

Requirements

change.This course is for senior data engineers, data architects, technical leads and even data engineering managers who want to move from building to designing, from tool familiarity to architectural reasoning, and from opinions to decisions they can defend in any room.
Data engineers who know the tools but want to level up into architectural thinking and design decisions.,Analytics engineers and data leads looking to expand their scope from pipeline-level to platform-level responsibility.,Engineers transitioning into data architect or staff engineer roles who need a structured, repeatable decision framework.,Teams preparing their data platforms for AI and GenAI workloads who need a principled, future-ready architecture approach.,Senior Engineering Managers, Data Directors & Technical Leads responsible for platform strategy and investment decisions who wants to gain deep data architectural knowledge and evaluate proposals critically, not just contextually.
Homepage

https://www.udemy.com/course/data-architecture-mastery-think-like-senior-data-architect/

   
   
   

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