DP-750T00: Implement data engineering solutions using Azure Databricks

About this course

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. Audience Profile 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.

Instructor-led training for professionals and companies in Chile. Request dates, schedules, seats and commercial terms. Training hours and daily scheduling follow Netsolutions planning and are confirmed in the commercial proposal.

Course syllabus

1. Set up and configure an Azure Databricks environment
2. Explore Azure Databricks
3. Understand Azure Databricks architecture
4. Understand Azure Databricks Integrations
5. Select and Configure Compute in Azure Databricks
6. Create and organize objects in Unity Catalog
7. Secure and govern Unity Catalog objects in Azure Databricks
8. Secure Unity Catalog objects
9. Govern Unity Catalog objects
10. Prepare and process data with Azure Databricks
11. Design and implement data modeling with Azure Databricks
12. Ingest data into Unity Catalog
13. Cleanse, transform, and load data into Unity Catalog
14. Implement and manage data quality constraints with Azure Databricks
15. Deploy and maintain data pipelines and workloads with Azure Databricks
16. Design and implement data pipelines with Azure Databricks
17. Implement Lakeflow Jobs with Azure Databricks
18. Implement development lifecycle processes in Azure Databricks
19. Monitor, troubleshoot and optimize workloads in Azure Databricks