Data Build Tool - DBT Complete Masterclass

Learn to transform raw data into analytics-ready models with dbt, Snowflake, and GitHub. Build production-grade pipelines with tests, documentation, and data catalogs, including macros, seeds, sources, snapshots, pre-hook, and post-hook.

Explore the complete dbt curriculum, from introduction and installation to modeling, materialization types, seeds and sources, and snapshots, tests, macros, jinja, dbt docs, and pre/post run hooks with Snowflake.

Define what dbt is, why it was created, and the problem it solves. Compare ETL and ELT, and outline when to use dbt core versus dbt cloud.

dbt core is the open-source, free option installed via pip and run in the terminal, while dbt cloud is paid SaaS with a browser-based IDE, scheduler, and collaboration features.

Set up dbt cloud on your local machine, sign up, log in, and create a project. Connect dbt to Snowflake and GitHub, and configure a demo connection for your workflow.

Initialize your dbt project in the cloud, create a feature branch, and explore folder structure with models and macros. Run dbt to build the demo schema and table in Snowflake.

learn the four dbt materialization strategies and how to choose the right one for a given use case, balancing performance, cost, and data freshness in Snowflake.

Explore the four dbt materialization types—view, table, incremental, and ephemeral—and learn how each balances freshness, storage, compute costs, and use cases in snowflake.

Create a view materialization in dbt using a staging model and ginger template with ref, then run and verify the virtual view that reads from the source without storing data.

Table materialization in dbt creates a physical table, dropping and rebuilding it on every run, offering fast queries for small datasets but costly full rebuilds with millions of rows.

discover how incremental materialization cuts cost by processing only new or changed rows in this dbt complete masterclass session, with strategies like append, merge, delete, and insert overhead.

Learn the append incremental strategy in dbt, building an orders-append model that inserts new rows from a snowflake raw orders table using an incremental condition with max shipped date.

Learn the insert_overwrite incremental strategy in dbt, partitioning by date to replace entire partitions with fresh data and applying it to staging models for orders.

Explore ephemeral materialization in dbt, which creates no database objects and is used to host lightweight, reusable logic that other models reference at compile time.

Learn how dbt seeds load CSV files into Snowflake as tables for static lookups and mappings, with version-controlled changes and reference SIDs like models.

Create csv in the seeds folder with headers country code, country name, region. Run dbt seed to generate a country codes table in the demo schema, then preview the data.

Explore dbt snapshots, a simple and scalable feature to track and store historical changes in data, enabling analysis of evolving records and slowly changing dimensions without etl.

Explore how dbt snapshots create a time machine for your data, preserving history and enabling SCD type 2 to track changes over time.