Implement data engineering solutions using Azure Databricks
DP-750(4 days)
Implement data engineering solutions using Azure Databricks
18.800 DKK
(excl. VAT)
Course calendar
Description
Azure Databricks is Microsoft's answer to a unified platform for data engineering, analytics and AI built on top of Apache Spark. With Unity Catalog as the central governance layer, Databricks has become a true enterprise tool, where security, lineage, audit and data sharing are managed in one place instead of being scattered across notebooks and workspaces. This course is Microsoft's official instructor-led course for data engineers who need to work end-to-end with the platform.
The course starts with the architecture behind Azure Databricks: how the account hierarchy, control plane and compute plane fit together, and which storage options you have through Unity Catalog. You work on selecting and configuring compute (serverless versus classic, autoscaling, Photon, runtime versions) and learn when the different types make sense for different workloads.
A significant part of the course covers Unity Catalog: how to organize catalogs, schemas, tables, views and volumes, how to secure them with row filtering, column masking, service principals and managed identities, and how to build governance with tags, ABAC policies, data lineage, audit logging and Delta Sharing. This is the part of the material that makes the difference between an experimental setup and a platform that operations and compliance teams can stand behind.
The second half focuses on the data work itself. You learn to design ingestion logic and choose between Delta Lake, Apache Iceberg and other table formats, implement partitioning and clustering, and handle slowly changing dimensions and temporal tables. You work with Lakeflow Connect, Auto Loader, Structured Streaming from Kafka and Event Hubs, change data capture, and orchestration with Lakeflow Spark Declarative Pipelines and Lakeflow Jobs. Finally, the course ties it all together with the development lifecycle: Git folders, branching, testing, Databricks Asset Bundles and CLI-based deployment across environments, plus monitoring and troubleshooting of Spark jobs, skew, spill and shuffle.
After the course, you can:
- Configure Azure Databricks workspaces and choose the right compute for different workloads
- Organize data in Unity Catalog with catalogs, schemas, tables, views and volumes
- Implement fine-grained access control with row filtering, column masking and ABAC
- Design and build ingestion pipelines with Lakeflow Connect, Auto Loader and Structured Streaming
- Model data with Delta Lake, partitioning, clustering and slowly changing dimensions
- Manage data quality with schema enforcement and pipeline expectations
- Orchestrate workloads with Lakeflow Jobs and Lakeflow Spark Declarative Pipelines
- Deploy pipelines across environments with Git and Databricks Asset Bundles
- Monitor and optimize Spark workloads, including skew, spill and shuffle
The course is aimed at data engineers who already have a basic knowledge of Azure Databricks workspaces and Unity Catalog, are comfortable with SQL and Python (including notebooks), and have basic knowledge of Azure security and Git.
Related exam
Prerequisites
To get the most out of the course, we recommend experience or knowledge equivalent to:
Module overview
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.
Lessons:
- 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
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.
Lessons:
- Secure Unity Catalog objects
- Govern Unity Catalog objects
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.
Lessons:
- 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
Master the complete lifecycle of building, deploying, and maintaining production-ready data pipelines in Azure Databricks—from design and orchestration to monitoring and optimization.
Lessons:
- 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
Free course advice
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