Design and deliver scalable data pipelines using Azure Databricks and the lakehouse architecture.
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.
Module 1: Set up and configure an Azure Databricks environment
Module 2: Secure and govern Unity Catalog objects in Azure Databricks
Module 3: Prepare and process data with Azure Databricks
Module 4: Deploy and maintain data pipelines and workloads with Azure Databricks
By the end of this course, learners will be able to:
Basic understanding of data and analytics concepts is recommended.
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.
