dbt Data Build Guide

dbt Data Build Guide is an educational application designed to teach data analysts, analytics engineers, and data platform developers how to transform data in modern cloud warehouses using dbt (data build tool).

The application is structured into eight comprehensive learning modules:
1. Introduction to dbt and Analytics Engineering: Explore ELT architecture, dbt Core vs dbt Cloud, modular SQL, and data warehouse adapters.
2. Setting Up Your dbt Project and Connections: Master dbt_project.yml, profiles.yml authentication, target environments (dev, prod), model layering (staging, intermediate, marts), and seeds.
3. Building Data Models in dbt: Deep dive into view, table, incremental, and ephemeral materializations, ref() and source() dependency management, and dimensional modeling.
4. Data Testing and Quality Assurance: Learn generic tests (unique, not_null, accepted_values, relationships), custom singular SQL tests, source freshness checks, and test severity configurations.
5. Documentation, Lineage, and Metadata: Generate interactive dbt docs, inspect the DAG, write Markdown doc blocks, declare exposures, and analyze manifest.json metadata.
6. Advanced dbt Features: Jinja, Macros, and Packages: Master Jinja templating, build custom macros, utilize cross-database functions, and manage packages like dbt-utils and dbt-expectations.
7. Incremental Models and Performance Optimization: Master is_incremental() logic, unique_key matching, incremental strategies (merge, delete+insert, insert_overwrite), and warehouse clustering/partitioning.
8. CI/CD, Production Deployment, and Orchestration: Implement Slim CI with state deferral (--defer), orchestrate with Airflow, Dagster, and Prefect, and deploy zero-downtime production jobs.

The package includes five interactive quizzes (125 total questions) to evaluate your mastery, along with an extensive 100-term glossary covering analytics engineering terminology.

This is an unofficial educational application and is not affiliated with, endorsed by, or sponsored by dbt Labs or the dbt project. All product names, logos, and brands are property of their respective owners.