Overview
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.
Why Choose KORNERSTONE
- Accredited, practitioner-led, expert certificate trainers to provide high quality training
- Official training material included
- Practical, scenario-based learning – focus on application, not just theory
- Excellent Passing Rate
Skills Covered
- Set up and configure an Azure Databricks environment
- Secure and govern Unity Catalog objects in Azure Databricks
- Prepare and process data with Azure Databricks
- Deploy and maintain data pipelines and workloads with Azure Databricks
Prerequisites
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.
Target Audience
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.
Course Curriculum
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
Module 2: Secure and govern Unity Catalog objects in Azure Databricks
Unity Catalog provides centralized governance and security for data assets in Azure Databricks. This module explores how to secure Unity Catalog objects through access control strategies, fine-grained permissions, credential management, and authentication mechanisms.
You’ll learn how to implement table and schema-level security, enforce row and column filtering, securely access secrets from Azure Key Vault, and authenticate data access using service principals and managed identities.
- Secure Unity Catalog objects
- Govern Unity Catalog objects
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
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
Dates & Locations
October 5, 2026 - October 8, 2026
November 2, 2026 - November 5, 2026
Exam & Certification
Microsoft Certified: Azure Databricks Data Engineer Associate
Demonstrate expertise in integrating and modeling data, building and deploying optimized pipelines, and troubleshooting and maintaining workloads in Azure Databricks.
- Level: Intermediate
- Product: Azure Databricks
- Role: Data Engineer
- Subject: Data engineering
