“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
Track: Databricks architecture-aligned learning planOfficial Source: Databricks
DatabricksAdvanced
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.
Duration
2 days
Level
Advanced
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
In Demand
Databricks
ArchitectureDA
Lakehouse design, modeling, data products
Databricks Data
MODEL
On this page
Ideal for
Data ArchitectData ArchitectureTailored Team DeliveryImplementation-Focused
Audience Profile
Built for these roles
Built for data architects who need to make reusable platform and lakehouse design decisions on Databricks.
Overview
Executive overview
Databricks architecture program focused on medallion design, domain modeling, governance-aware data products, serving patterns, and lakehouse architecture decisions.
Readiness
Prerequisites
- Strong data architecture or senior data engineering experience.
- Familiarity with lakehouse and analytics-platform concepts.
- Working knowledge of Databricks data and governance primitives is recommended.
Program Outcomes
Capabilities your teams will gain
Design stronger lakehouse and medallion architecture patterns
Align data-product structure with governance and ownership boundaries
Make better serving, warehousing, and analytics architecture decisions on Databricks
Improve long-term architectural consistency for Databricks data platforms
Curriculum
Curriculum roadmap
1
Lakehouse and medallion architecture design
2
Modeling, governance, and Unity Catalog patterns
3
Serving-layer and data-product architecture
4
Architecture standards for analytics and AI workloads
1Module 1
Define lakehouse architecture foundations
+
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
+
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
+
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
+
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
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Implementation-focused labsPrivate cohort deliveryAdvanced practitioner depthBusiness outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Helps teams design more durable lakehouse architectures by aligning storage, modeling, governance, and serving decisions with real Databricks platform capabilities.
- •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
Credentials
Certification & official source
- •Databricks architecture-aligned learning plan
Aligned to the official Databricks training catalog and certification guidance for this program.
View Databricks Training SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
