Databricks Interactive Architecture Design Workshop

In this course, you will gain a comprehensive understanding of how to design, scale, and operationalize end-to-end feature engineering pipelines on the Databricks platform. The curriculum is structured across three progressive modules: mastering the fundamentals of Spark’s distributed execution and optimization, implementing scalable data ingestion with Auto Loader and declarative Lakeflow pipelines, and advancing to production-grade MLOps with the Databricks Feature Store.

You will engage in hands-on learning experiences such as debugging Spark performance with the Catalyst Optimizer and Spark UI, building robust Bronze-Silver-Gold medallion architectures with automated quality checks, and implementing scalable feature transformations using SparkML. The course culminates in deploying real-time feature serving through Online Feature Stores, defining FeatureSpecs with on-demand transformations, and applying governance and lineage tracking with Unity Catalog.

In this course, you will explore the fundamentals of data governance and Unity Catalog. You will learn about the evolution and purpose of data catalogs, the architecture and components of Unity Catalog, and the importance of data quality, security, compliance, lineage, and auditing in governance.

Languages Available: English | 日本語 | 한국어

In this course, you will explore how Unity Catalog enables secure, centralized data governance and fine-grained access control on Databricks. You will learn about table and volume types, catalog and schema configuration, group-based access management, and strategies for migrating existing access controls into Unity Catalog. The course also explains how to design and apply fine-grained controls such as row-level security, column masking, and attribute-based access control, how to combine these mechanisms across data and AI assets, and how to align them with broader governance requirements for compliant, scalable access management.

Languages Available: English | 日本語 | 한국어