Data Modeling Strategies

This course walks practitioners through the full spectrum of data modelling approaches on the Databricks Data Intelligence Platform - from classical data warehouse techniques (Inmon, Kimball, Data Vault 2.0), through Feature Store-driven ML use cases, to productising data via Data Products on Unity Catalog.

Each modelling approach is introduced with a lecture, then reinforced with a hands-on demo against a shared dataset (TPC-H samples). The course finishes with a comprehensive end-to-end lab that exercises ERM, dimensional modelling, Data Vault 2.0, and the Feature Store in a single integrated workflow.

Note:

1. The course includes practice labs that the learners should perform after going through the entire course.

2. For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

Languages Available: English | 日本語 | Português BR | 한국어

Skill Level

Associate

Duration

Prerequisites

In this course, the content was developed for participants with these skills/knowledge/abilities:

• Working knowledge of SQL and relational database concepts

• Familiarity with Databricks fundamentals (workspaces, notebooks, Unity Catalog basics)

• Conceptual understanding of OLTP vs OLAP and the medallion architecture

• Basic exposure to Python and PySpark is helpful but not required

• Awareness of dimensional modelling concepts is helpful but not required

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Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

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Public and private courses taught by expert instructors across half-day to two-day courses

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Upcoming Public Classes

Data Warehousing Practitioner

Data Interoperability with Unity Catalog

This course explores how to achieve data interoperability between systems using Databricks Unity Catalog as a unified governance layer, using both Delta and Iceberg open table formats. Participants will learn to implement, manage and optimize a data environment where tables can be accessed by both Delta and Iceberg clients, enabling cross-platform analytics and eliminating data silos.

This course is designed for Data Engineers, Platform Administrators, and Analytics Engineers responsible for implementing and managing multi-format data architectures. You will gain the skills to use Unity Catalog for unified governance across Delta and Iceberg table formats and across federated external systems.

Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

Paid & Subscription

Lab

Associate

Architecting Data Warehouses for Large-Scale Deployments

This course covers performance optimization, cost control, and security for large-scale data warehousing deployments.This course is designed for Data Warehousing practitioners responsible for managing Databricks environments serving hundreds or thousands of users across multiple business units. You will gain the skills necessary to efficiently scale data warehousing operations while maintaining high performance, cost-effectiveness, and compliance with security standards.

Paid & Subscription

Lab

Associate

Lakebase App Integration and Management

In this course, you will learn how to integrate a Databricks App with Lakebase, Databricks' fully managed, cloud-native PostgreSQL OLTP database. This course assumes some familiarity with Databricks Apps and Lakebase and will therefore not cover the basic of those pieces; instead we'll focus on integrating the two to deliver an app that performs basic data collection and captures it in a Lakebase table. An overview of PostgreSQL and safety best practices will be emphasized.

Paid & Subscription

Lab

Associate