dbt-Beginner to Pro

Register for the DVT club and explore hands-on setup by creating a free account, verifying your email, and logging in, with upcoming coverage of the personal best option.

Set up a new dbt project in Liberty by connecting to Snowflake with a warehouse, default database, and schema, initialize the project to create directories, and push code via GitHub.

Learn the default dbt project folder structure in liberty cloud, including models, tests, macros, seeds, and snapshots, and how to organize code accordingly.

Navigate the dbt environment setup UI, manage development and deployment environments, and configure Snowflake connections in personal profiles and project settings.

Explore a three-layer data flow—landing, semantic layer, and model—transforming source data into reporting-ready schemas, with temporary developer databases and override connection properties for isolated development.

Create your first dbt model by declaring sources, organizing models in a semantic layer, and materializing them as tables in Snowflake. Learn source functions and committing changes via GitHub.

Alter dbt model shows adding audit columns and an md5 key to the stage customer table in Snowflake, enabling automatic load-time metadata and consistent handling across related tables.

Learn how to use tags in dbt to selectively run semantic models by assigning a tag to folders or models and executing only those tagged models.

Create join models and generate lineage diagrams in dbt, using the rev and source functions to link semantic layers and visualize how customer ranks derive from orders.

Discover how dbt builds a lineage diagram and an execution dependency model to enable parallel execution, optimize dependency-driven runs, and trigger downstream models with the plus sign.

learn how dbt snapshot preserves history by preventing new data from erasing past records, using a simple customer subscription table and two creation methods.

Apply dbt snapshots to track subscription changes by using the timestamp strategy on the source last update time alias, configure target schema, and run the snapshot.

Continue with dbt timestamp snapshot type two and inspect snapshot's maintenance columns. Update a record to see versioned changes with valid from and valid to, as dbt handles the rest.

Learn how to implement dbt snapshots with the check_cols strategy on a source table lacking last update columns, using key attributes and check attributes to detect changes.

Explore dbt macros as reusable code you call with parameters across projects, and build a simple audit framework that inserts a record before a model run and updates after.

Learn how to create a dbt macro that inserts or updates run history in the job rerun history table, using ginger coding and dynamic max run id logic.

Learn how to invoke a macro to insert and update records in a model, pass parameters, and review execution history and timestamps to understand timing and order issues.

Learn to use pre hook and post hook to control macro execution around dbt models, apply consistent pre/post statements across the semantic folder, and review the execution history.

Liberty environment variables to parametrize database and schema names across development and job execution environments, avoiding hard-coded values and enabling automatic routing to semantic, snapshot, and raw schemas.

Learn how to declare and use environment variables in dbt to parameterize database and schema settings, replace hardcoded values, and run code against development environments.

Create and run a dbt job in a custom environment. Observe how environment variables replace across development and the job execution environment, using a feature branch and custom schema.

Use a macro to override the default dbt schema naming with a custom schema during job execution, then commit changes to GitHub so Liberty applies environment variable overrides.

Explore how dbt run and snapshot results produce artifacts stored in the liberty target folder, including run results and manifest, and how to download them without GitHub commits.

Explore the Liberty Cloud API for creating and managing jobs, listing projects, triggering jobs, and downloading artifacts via API keys.

Trigger a dbt api job with the dvd api key and monitor its run status to completion, retrieving run results and artifacts via Python, Java, or Databricks on Liberty Cloud.

Discover how a dbt package offers plug-and-play reusable utilities for data manipulation, how to declare it in a packages.yaml, install with dvd deps, and run with plus model prerequisites.

Seed data loads a one-time reference file into a lookup table, such as country_state, using dbt seed to populate the landing layer without coding.

Learn how to use dbt incremental model to load new seed data into the raw landing and stage semantic layer, with a config block overriding materialization for a single model.

Learn how dbt integrates with git-based versioning tools like GitHub and GitLab, enabling branch creation, commits, pull requests, and continuous integration through scheduling and webhooks.