Guides

dbt Cloud is the fastest and most reliable way to deploy your dbt jobs and dbt Core is a powerful open-source tool for data transformations. With the help of a sample project, learn how to quickly start using dbt and one of the most common data platforms.

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Build, test, document, and promote adapters

Airflow and dbt Cloud

Quickstart for dbt Cloud and Azure Synapse Analytics

Quickstart for dbt Cloud and BigQuery

Building dbt packages

Quickstart for dbt Core using GitHub Codespaces

Move from dbt Core to dbt Cloud: What you need to know

Move from dbt Core to dbt Cloud: Get started

Move from dbt Core to dbt Cloud: Optimization tips

Create new materializations

Customizing CI/CD with custom pipelines

Customize dbt models database, schema, and alias

Quickstart for dbt Cloud and Databricks

Optimize and troubleshoot dbt models on Databricks

Leverage dbt Cloud to generate analytics and ML-ready pipelines with SQL and Python with Snowflake

Debug errors

Debug schema names

Build a data lakehouse with dbt Core and Dremio Cloud

Quickstart for the dbt Explorer workshop

Use Databricks workflows to run dbt Cloud jobs

Quickstart for dbt Core from a manual install

Quickstart with dbt Mesh

Quickstart for dbt Cloud and Microsoft Fabric

Migrate from dbt-spark to dbt-databricks

Migrate from DDL, DML, and stored procedures

Productionize your dbt Databricks project

Coalesce: Quickstart for dbt Cloud CLI

Quickstart for dbt Cloud and Redshift

Refactoring legacy SQL to dbt

Create Datadog events from dbt Cloud results

Trigger PagerDuty alarms when dbt Cloud jobs fail

Get started with Continuous Integration tests

Set up your dbt project with Databricks

Legacy dbt Semantic Layer migration guide

Integrate with dbt Semantic Layer using best practices

Quickstart for the dbt Cloud Semantic Layer and Snowflake

Quickstart for dbt Cloud and Snowflake

Quickstart for dbt Cloud and Starburst Galaxy

Quickstart for dbt Cloud and Teradata

Use Jinja to improve your SQL code

Post to Microsoft Teams when a job finishes

Refresh a Mode dashboard when a job completes

Refresh Tableau workbook with extracts after a job finishes

Post to Slack with error context when a job fails