Data Modeling Strategies

In this course, you will learn how to design scalable, analytics-ready data models using the Databricks Lakehouse Platform. The course is structured into three modules covering foundational data modeling principles, advanced warehousing and machine learning use cases, and modern data product design. You’ll explore core methodologies like Inmon’s CIF, Kimball’s dimensional modeling, and Data Vault 2.0, along with practical demos on implementing these strategies in Databricks.

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:

• Basic knowledge of data warehousing concepts, including schemas, ETL processes, and business intelligence workflows.

• Familiarity with SQL, including table creation, joins, constraints, and data manipulation using queries.

• Basic experience with Databricks or similar cloud-based data platforms.

• Fundamental knowledge of Lakehouse architecture and Delta Lake operations.

• Exposure to Python and PySpark for data processing and transformation tasks (recommended but not mandatory).

Outline

Data Warehouse Data Modeling

• Lakehouse Architecture Recap

• Data Warehousing Modeling Overview

• Inmon’s Corporate Information Factory

• Kimball’s Dimensional Modeling

• Dimensional Modeling

• Data Vault 2.0

Modern Data Architecture Use Cases

• Feature Store

• Combining Approaches

Data Products

• Defining Data Products

• Summary and Next Steps

• Data Warehousing Modeling with ERM and Dimensional Modeling in Databricks

Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	Jun 15	01 PM - 05 PM (Europe/London)	-	English	$750.00
Jul 06	01 PM - 05 PM (Australia/Sydney)	-	English	$750.00
Jul 06	09 AM - 01 PM (America/New_York)	-	English	$750.00

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Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	Jun 15	01 PM - 05 PM (Europe/London)	-	English	$750.00
Jul 06	01 PM - 05 PM (Australia/Sydney)	-	English	$750.00
Jul 06	09 AM - 01 PM (America/New_York)	-	English	$750.00

Registration options

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Upcoming Public Classes

Data Warehousing Practitioner

SQL Programming and Procedural Logic

In this course, you will explore core SQL programming and procedural logic skills within Databricks, focusing on how to build modular, maintainable, and production-ready analytics workflows. You’ll start by learning about key SQL constructs—such as Common Table Expressions (including advanced recursive CTEs), session-scoped objects like temporary views and tables, and User Defined Functions—that make your SQL development cleaner and more reusable. The course then guides you through advanced SQL scripting techniques using compound statements, variables, control flow, conditionals, loops, and robust error handling to create structured and flexible SQL processes. You will also learn how to guarantee data consistency across these multi-step pipelines using ACID-compliant multi-statement transactions. Finally, you’ll discover how to encapsulate logic into SQL Stored Procedures and orchestrate complex workflows with Lakeflow Jobs and migration strategies, transforming legacy tasks into modular, automated pipelines that leverage the full capabilities of the Databricks platform.

Data Warehousing Practitioner

Data Interoperability with Unity Catalog

This course explores how to achieve seamless 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.

Building Enterprise Applications with Databricks Apps

This course introduces Databricks Apps, a new feature that empowers developers to build and deploy secure data and AI applications directly on the Databricks platform. It is primarily designed for data scientists, data engineers, and developers who need to create custom applications that make data insights accessible to non-technical users within their organizations.The course begins with an overview of the main components of Databricks Apps, followed by the deployment of a simple app using available templates. Next, you will learn how to build and deploy a data-driven application that integrates with other platform features. The course then covers how to set up an IDE for Databricks Apps, and how to edit and deploy an app using an IDE. It concludes with how to implement security and best practices for an enterprise app.