DBT (Data Build Tool) from a Beginner to Expert

Explain how dbt prevents data swamps by establishing a data catalogue, metadata descriptions, and lineage, while enabling analysts to scale with sql, collaborate, and leverage cloud warehouses for fast analytics.

Explore dbt main configurations, including table, view, ephemeral, and incremental models, and see how dbt run handles them with general and adapter specific options like alias, tag, docs, and schema.

Explore singular tests in dbt with case-specific sql files that fail when they return results, referencing fact_transactions and values like 3, and note that dbt test scans the tests directory.

Explore how dbt snapshots implement slowly-changing dimensions by tracking historical employee data through valid_from, valid_to, and updated_at, using the dbt seed command.

Explore the dbt development commands, including dbt run, dbt seed, dbt test, dbt compile, dbt snapshot, and dbt build, and learn when and how to apply them in production.

Master the dbt selection criterion using select and exclude to run only needed models like dim_packs; explore graph and set operators, unions, intersections, and the @ operator for dbt runs.

Learn to manage multiple dbt environments by replicating dev to prod, configuring profiles, and running dbt build with full refresh to deploy to production.

Explore dbt plugins, focusing on dbt-sugar, a Python package that auto-generates descriptions, column details, and tests for models. Learn its setup and compatibility with Python 3.6+ and PostgreSQL, Snowflake, Redshift.