Dbt Data Build Tool Study Notes

Building a Star Schema with dbt

How to design fact and dimension tables in dbt using surrogate keys, explicit grain, incremental materialization, and referential-integrity tests.

CI/CD for dbt Projects

How to build continuous integration and deployment pipelines for dbt projects using slim CI, automated testing, and gated production deploys.

Connecting to a Warehouse

How dbt uses adapters and profiles.yml to authenticate against a data warehouse, and how to verify the connection with dbt debug.

Custom Data Tests

When the four built-in generic tests aren't enough, dbt lets you write singular tests as standalone SQL files or custom generic tests as reusable, parameterize…

dbt Best Practices

The project structure, layering, testing, and deployment conventions that keep a dbt project maintainable as it scales past a handful of models.

dbt Cloud vs dbt Core

A practical comparison of dbt's two deployment models — the self-hosted open-source CLI versus the managed dbt Cloud platform — and how to choose between them.

dbt Interview Questions

Common dbt interview questions covering concepts, materializations, testing, and macros, with the reasoning behind strong answers.

dbt Packages

dbt packages let you install and reuse pre-built models, macros, and tests published by others, from the dbt Hub or a private Git repo, via a simple packages.y…

dbt Quick Reference

A lookup sheet of the CLI commands, YAML config keys, and Jinja functions used every day in a dbt project.

dbt Semantic Layer Basics

The dbt Semantic Layer lets you define metrics once, on top of governed models, and query them consistently from any connected BI tool via MetricFlow instead o…

dbt vs Traditional ETL

How dbt's ELT, SQL-in-Git approach differs from traditional GUI-based ETL tools like Informatica or SSIS in compute cost, collaboration, and testing.

Documentation with dbt docs

dbt turns descriptions in your YAML files and standalone doc blocks into a browsable, auto-generated documentation site with an interactive lineage graph.

Environments and Deployment

How dbt separates development, staging, and production environments using targets, schemas, and deployment jobs so changes are validated before reaching produc…

Incremental Models In-Depth

Incremental models process only new or changed rows on subsequent runs instead of rebuilding the full table, using is_incremental() and configurable strategies…

Installing dbt and Project Setup

How to install dbt Core with the correct database adapter, scaffold a new project with dbt init, and understand the role of profiles.yml.

Jinja Templating in dbt

Learn how dbt uses the Jinja templating engine to add loops, conditionals, and variables to plain SQL, turning static queries into reusable, environment-aware…

Macros

Learn how dbt macros turn repeated Jinja + SQL patterns into reusable, function-like building blocks, and how community packages like dbt_utils extend them fur…

Materializations: View, Table, Incremental

Understand how dbt's view, table, and incremental materializations trade off build cost against query performance, and when to use each.

Orchestrating dbt Runs

How to schedule and sequence dbt commands reliably in production using dbt Cloud jobs, Airflow, Dagster, or cron, including selectors and freshness checks.

Performance and Cost Optimization

Techniques for reducing dbt run time and warehouse spend, including materialization strategy, incremental models, clustering, and query cost monitoring.

ref() and source() Functions

Learn how dbt's ref() and source() functions build the project's dependency graph and enable environment-safe, portable SQL.

Schema Tests

Schema tests are YAML-declared assertions — unique, not_null, accepted_values, and relationships — that dbt compiles into SQL to automatically validate your da…

Seeds

dbt seeds load small, static CSV files version-controlled in your repo directly into your warehouse as tables, ideal for reference and mapping data that doesn'…

Snapshots

dbt snapshots implement Type 2 slowly changing dimensions, capturing row-level changes over time so you can query what a record looked like at any past point.