Data Modeling with the data build tool (dbt) and Microsoft Fabric

Data Modeling with the data build tool (dbt) and Microsoft Fabric

The simplicity of the data build tool (dbt) makes it very attractive for companies that want to create a medallion architecture using just the ANSI SQL syntax that they already used to. Today, we are going to talk about the dbt-core framework written in Python.

Data materialization is the act of transforming the data in the database or warehouse into a table, view, materialized view, snapshot or etc. Learn the use case for each of these model types.

Data quality is very important for a data warehouse. There are built in and custom data tests that can be used against your model.

Model Documentation is a very important part of any software development life cycle. If you enforce your developers to create YAML files for each model, you will end up with a data dictionary. Learn how to export that information to an HTML for your organization.

The real power is the fact that a simple change of the ODBC driver and the python package (adapter), the developer can move code from one service to another. Both the Fabric Database and Farbric Warehouse (Spark) will be the targets of my open source project for the Chinook database schema.

Attend this presentation to raise gain knowledge on how to use the dbt framework with Microsoft Fabric.

Speaker
John Miner

John Miner is a Senior Data Architect at Insight Digital Innovation helping corporations solve their business needs with various data platform solutions.

He has over thirty years of data processing experience, and his architecture expertise encompasses all phases of the software project life cycle, including design, development, implementation, and maintenance of systems.

His credentials include undergraduate and graduate degrees in Computer Science from the University of Rhode Island. Also, he has earned certificates from Microsoft for Database Administration (MCDBA), System Administration (MCSA), Data Management & Analytics (MCSE) and Data Science (MPP).

John has been recognized with the Microsoft MVP award seven times for his outstanding contributions to the Data Platform community.

When he is not busy talking to local user groups or writing blog entries on new technology, he spends time with his wife and daughter enjoying outdoor activities. Some of John’s hobbies include wood working projects, crafting a good beer and playing a game of chess.