Become a Data Architect: Design, Decide & Defend Platforms

Most data courses teach you tools. This one teaches you to decide.

A senior data architect's job isn't building pipelines — it's making the right trade-offs and defending them to the people who sign the cheques. Should this be a warehouse, a lakehouse, or a mesh? Delta, Iceberg, or Hudi? Batch, incremental, or CDC? Streaming — or is that just expensive theatre? How much governance is enough? Is your platform even ready for AI? Modern Data Architecture Mastery is built around those decisions, end to end.

Across 22 modules and 110 lessons, you'll work the way real architects do: weighing latency against cost, flexibility against control, and "best practice" against what actually fits this company. Every act closes with a hands-on Architecture Review Workshop, and the whole course builds toward a capstone where you design a platform and defend it in front of a CFO, a CISO, and a CTO.

What makes this course different

• Decision-first, not tool-first. You leave with a reusable Architect Toolkit — ADR templates, a trade-off/selection matrix, cost and AI-readiness checklists — not a list of vendor features that'll be stale next quarter.
• Vendor-neutral and current. Patterns over products: the same reasoning applies whether you're on Snowflake, Databricks, BigQuery, or Fabric.
• AI-ready by design. A full act on architecting for AI — feature stores, vector databases, RAG and LLM serving, and the governance that keeps probabilistic systems safe.
• War stories that stick. The $50k query, the CDC loop that melted production, the mesh that became a mess — and the architectural guardrails that prevent each.

You'll learn to

• Choose between warehouse, lakehouse, data mesh, and fabric using a trade-off matrix, not hype.
• Pick table formats (Delta/Iceberg/Hudi), ingestion (batch/incremental/CDC), and modeling patterns (star, vault, OBT) for the situation in front of you.
• Decide streaming-vs-batch by latency, volume, and cost — and know when real-time isn't worth it.
• Architect for cost, performance, security, governance, and reliability with explicit budgets and SLOs.
• Design an AI-ready platform: feature stores, vector databases, RAG/agent serving, and AI governance.
• Write Architecture Decision Records and run a 9-part architecture review that survives CFO/CISO/CTO scrutiny.

Who's teaching

Built by Snowbrix Academy and taught by Amit — a credentialed practitioner (SnowPro Core, 2x Databricks-certified) who builds production data platforms for a living. Every pattern here is one you can defend on Monday.

If you're a data engineer or analytics engineer ready to step up to architecture-level thinking — to stop asking "which tool?" and start asking "which trade-off?" — this is your course.