Build a data lakehouse with dbt Core and Dremio Cloud
Learn how to build a data lakehouse with dbt Core and Dremio Cloud.
Learn how to build a data lakehouse with dbt Core and Dremio Cloud.
When you have dbt code that might help others, you can create a package for dbt using a GitHub repository.
Learn about errors and the art of debugging them.
Learn how to debug schema names when models build under unexpected schemas.
Learn how to migrate from dbt-spark to dbt-databricks.
Learn how to transform from a historical codebase of mixed DDL and DML statements to dbt models, including tips and patterns for the shift from a procedural to a declarative approach in defining datasets.
Learn how to move from dbt Core to dbt Cloud and what you need to get started.
Use this guide to learn how to optimize your dbt Cloud experience and get answers to common questions.
Use this guide to understand the considerations and methods you need to move from dbt Core to dbt Cloud.
Learn more about optimizing and troubleshooting your dbt models on Databricks
Learn how to deliver models to end users and use best practices to maintain production data.
Connecting your warehouse to dbt Core using the CLI.
Introduction
Learn more about setting up your dbt project with Databricks.
Learn how to use Databricks workflows to run dbt Cloud jobs
Learn how to improve your SQL code using Jinja.