Databricks Academy offers the free Data Modeling Strategies course to help data practitioners understand and apply different modelling approaches on the Databricks Data Intelligence Platform. It covers a broad range of strategies, from traditional warehouse modelling methods like Inmon, Kimball, and Data Vault 2.0 to modern use cases such as Feature Store and Data Products on Unity Catalog.
Youโll learn to:
- Compare core modelling approaches: Understand the differences between Inmon, Kimball, and Data Vault 2.0, and when each approach fits best.
- Build modern Lakehouse data models: Learn how modelling fits into the Bronze, Silver, and Gold layers, including relational integrity, star schemas, and vault structures.
- Support ML and data product use cases: Explore how Feature Store and Data Products on Unity Catalog extend modelling into machine learning and governed sharing.
- Choose the right strategy for the job: See how warehouse, vault, feature, and product-based approaches can work together in a practical Lakehouse architecture.
Designed for:
- Data architects and data practitioners working on Databricks
- Users with working knowledge of SQL and relational database concepts
- Learners familiar with Databricks fundamentals and medallion architecture
Course format & details:
- Syllabus: 3 sections | 17 lessons
- Duration: 2 hours
- Skill level: Associate
- Cost: Free
- Includes labs: No
- Languages: English, Japanese, Portuguese BR, Korean
Recent updates:
- Framed Metric Views, Feature Store, and Delta Sharing as Gold-layer pillars for BI, ML, and collaboration
- Updated the course to use Serverless compute
Important note:
- For SCORM lecture files, close the SCORM window after completing the content. Do not click Next Lesson, as that may prevent the module from being marked complete.
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