Learn dbt core using Paradime

Paradime provides the ultimate operating system for analytics teams, unifying data exploration, dbt modeling, dependencies, and production deployment in one place.

Explore dbt, the data build tool, for ELT transformations in the data warehouse with SQL and Jinja templating, and compare dbt core and dbt cloud for collaboration and automation.

Discover Paradigm's home page and use the editor, lineage, bolt, and SQL areas to build dbt models, assess downstream impacts, and speed up exploratory work.

Explore the Paradime editor's general features, including the dbt project layout and VS Code–style UI. Learn how to switch themes and keymaps, and navigate models and demo files.

Explore the editor features in Paradime, including docs, the data explorer, and the lineage graph, to visualize upstream dependencies, annotate changes, and compare branches for safe dbt development.

Explore bolt, Paradigm's scheduler, to manage hourly and daily schedules, inspect failures, view logs, annotate issues, and notify colleagues, then explore the SQL area for data model insights.

Explore the SQL workbench in Paradigm to preview data from the fact orders table, run queries, and inspect decompiled and compiled SQL with the jaffle model.

Log in to paradigm, set up the workspace and region, and create a fresh private git repository. Connect deploy keys to link paradigm with BigQuery and start dbt core work.

Build a dimensional model with dbt and Paradigm, connecting data sources to a cloud data lake and staging layer to a dimensional data warehouse.

Build dbt models within a dimensional data warehouse using Paradigm, initialize a repository, configure BigQuery with OAuth, and push changes via git to the project dataset.

Toggle between git lite and git advanced, then pull changes from remote to the main branch. Verify the repository is clean with git status.

Configure dbt_project.yml to enable staging and warehouse models, using materialized tables in staging_northwind and warehouse_northwind; create the dim date view to support dimensional modeling of dims and facts.

Commit dbt_project.yml changes by creating a feature branch, pushing, and merging to main via a pull request, validating a YAML update that includes staging and warehouse models.

Create a staging layer in dbt by using a feature branch, the terminal, and a staging folder with source yaml, mapping Northwind data and adding ingestion timestamps.

Run gpt models to materialize staging tables in BigQuery, then commit changes, push to origin, create and merge a pull request, and prepare to build the warehouse layer.

Build warehouse layer by creating dim customer, dim date (as a view), and fact purchase order tables, using a source CTE and selecting a unique record from the customer source.

Commit code changes to reflect data lineage, view the fact purchase order lineage with source and staging tables, and annotate for team review and Slack collaboration.

Explore the end-to-end demonstration of Paradime in dbt core, highlighting its capabilities to speed up analytics, engineering, and workflow, and gain insight into core features for daily use.