“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Microsoft Official Curriculum
Role-Based Certification PrepTrack: Microsoft Certified: Azure Databricks Data Engineer Associate (beta)Official Source: Microsoft Learn
MicrosoftIntermediate
Implement data engineering solutions using Azure Databricks
Master end-to-end data engineering with Azure Databricks and Unity Catalog. This course moves from foundational setup to production deployment, covering environment configuration and enterprise-grade governance. Learn to build robust ingestion pipelines, implement security with Unity Catalog, and deploy optimized workloads. By the end, you will have the practical skills to implement, secure, and maintain scalable lakehouse solutions that meet rigorous enterprise requirements.
Duration
4 days
Level
Intermediate
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
Databricks
Data EngineeringDB
Azure Databricks Data Engineer Associate (beta)
Azure Databricks
DBX
On this page
Ideal for
Data EngineerData EngineeringCertification ReadinessTailored Team Delivery
Audience Profile
Built for these roles
The target audience is data engineers who have fundamental knowledge of data analytics concepts, a basic understanding of cloud storage, and familiarity with data organization principles. They should be comfortable working with SQL and have experience using Python, including notebooks, for data engineering tasks. Learners are expected to have a good understanding of Azure Databricks workspaces and Unity Catalog, along with familiarity with data access patterns and core data engineering and data warehouse concepts. In addition, they should have foundational knowledge of Azure security, including Microsoft Entra ID, and be familiar with Git version control fundamentals.
Overview
Executive overview
As a candidate for this Microsoft Certification, you should have subject matter expertise in integrating and modeling data, building and deploying optimized pipelines, and troubleshooting and maintaining workloads in Azure Databricks.
Program Outcomes
Capabilities your teams will gain
Set up and configure an Azure Databricks environment
Secure and govern Unity Catalog objects
Prepare and process data
Deploy and maintain data pipelines and workloads
Curriculum
Curriculum roadmap
1
Set up and configure an Azure Databricks environment
2
Secure and govern Unity Catalog objects
3
Prepare and process data
4
Deploy and maintain data pipelines and workloads
1Module 1
Set up and configure an Azure Databricks environment
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Module 1
Set up and configure an Azure Databricks environment
Build a solid foundation in Azure Databricks by understanding its architecture, integrations, compute options, and data organization capabilities. Learn how Azure Databricks provides a unified platform for data engineering, analytics, and AI workloads in the cloud.
- Explore Azure Databricks
- Understand Azure Databricks architecture
- Understand Azure Databricks Integrations
- Select and Configure Compute in Azure Databricks
- Create and organize objects in Unity Catalog
2Module 2
Secure and govern Unity Catalog objects in Azure Databricks
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Module 2
Secure and govern Unity Catalog objects in Azure Databricks
Learn to implement comprehensive security and governance for your data assets in Azure Databricks using Unity Catalog. Master access control strategies, fine-grained permissions, credential management, and governance practices to build a secure and compliant data platform.
- Secure Unity Catalog objects
- Govern Unity Catalog objects
3Module 3
Prepare and process data with Azure Databricks
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Module 3
Prepare and process data with Azure Databricks
Master the essential skills to build robust, scalable data engineering solutions with Azure Databricks and Unity Catalog. Learn to design effective data models, ingest data from diverse sources, transform raw data into analytics-ready formats, and ensure data quality across your lakehouse architecture.
- Design and implement data modeling with Azure Databricks
- Ingest data into Unity Catalog
- Cleanse, transform, and load data into Unity Catalog
- Implement and manage data quality constraints with Azure Databricks
4Module 4
Deploy and maintain data pipelines and workloads with Azure Databricks
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Module 4
Deploy and maintain data pipelines and workloads with Azure Databricks
Master the complete lifecycle of building, deploying, and maintaining production-ready data pipelines in Azure Databricks—from design and orchestration to monitoring and optimization.
- Design and implement data pipelines with Azure Databricks
- Implement Lakeflow Jobs with Azure Databricks
- Implement development lifecycle processes in Azure Databricks
- Monitor, troubleshoot and optimize workloads in Azure Databricks
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Certification readinessImplementation-focused labsPrivate cohort deliveryIntermediate practitioner depth
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Builds current Microsoft credential readiness for Azure Databricks Data Engineer Associate (beta) using the official Microsoft Learn skill outline.
- •Align labs to your Microsoft tenant and workload scenarios
- •Add readiness checks and exam preparation reviews
- •Extend delivery with role-specific implementation workshops
Credentials
Certification & official source
- •Microsoft Certified: Azure Databricks Data Engineer Associate (beta)
Aligned to the official Microsoft Learn course and learning path for this program.
View Official Microsoft Learn PageResources
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
Banking & Finance
Representative Enterprise Banking Team
The focus was not just on tooling knowledge, but on helping teams work from a shared operating model as they adopted a more modern data platform.
- Clearer platform operating model across teams
- Improved confidence in modern data stack adoption
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
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
