Data Architecture with Databricks
This program is designed for architects shaping enterprise data foundations on Databricks. It focuses on data-domain design, medallion architecture, governance-aware modeling, serving patterns for analytics and AI, and the architectural trade-offs that influence long-term scalability and maintainability.
Certification
Databricks architecture-aligned learning plan
Delivery
Virtual, On-site, or Hybrid
Duration
2 days
Product
Databricks Data Intelligence Platform
Role
Data Architect
Databricks
ArchitectureLakehouse design, modeling, data products
Databricks Data
Best Fit
Audience Profile
Who This Program Is For
Built for data architects who need to make reusable platform and lakehouse design decisions on Databricks.
Overview
Program Summary
Databricks architecture program focused on medallion design, domain modeling, governance-aware data products, serving patterns, and lakehouse architecture decisions.
Course Outline
Complete Module Sequence
Review the full module sequence for this program, including the primary topic coverage in each module where available.
1Module 1
Define lakehouse architecture foundations
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Module 1
Define lakehouse architecture foundations
Set the structural patterns and medallion design choices that shape how data moves and matures across the platform.
- Lakehouse and medallion architecture design
2Module 2
Model data with governance in mind
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Module 2
Model data with governance in mind
Align data structures, ownership boundaries, and governance capabilities to support trusted and reusable data assets.
- Modeling, governance, and Unity Catalog patterns
3Module 3
Design serving and data-product layers
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Module 3
Design serving and data-product layers
Connect architecture decisions to how analytics, BI, and AI consumers will use governed data products on Databricks.
- Serving-layer and data-product architecture
4Module 4
Standardize architecture for broader adoption
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Module 4
Standardize architecture for broader adoption
Build the standards that help engineering and analytics teams deliver more consistently on a shared Databricks foundation.
- Architecture standards for analytics and AI workloads
Coverage Areas
Topic Coverage
Coverage Item 1
Lakehouse and medallion architecture design
Coverage Item 2
Modeling, governance, and Unity Catalog patterns
Coverage Item 3
Serving-layer and data-product architecture
Coverage Item 4
Architecture standards for analytics and AI workloads
Customization
Adapt This Program for Your Team
We can adapt this program around your team structure, platform priorities, delivery goals, and the scenarios your people need to work through in practice.
- •Use your data-domain model and platform constraints
- •Add analytics-serving and BI consumption architecture
- •Extend into data-product ownership, stewardship, and governance rollout decisions
