dbt (data build tool) has become the lingua franca of modern data transformation. In the space of five years, it went from an open-source experiment to the standard tool used by thousands of data teams worldwide. Understanding dbt, what it does, why it works, and how to use it strategically, is essential for any CDO overseeing a modern data platform.
dbt is a transformation tool. It sits in the T of ELTELTELT (Extract, Load, Transform) is a data integration pattern where raw data is loaded into a target system first, then transformed inside it using the platform's compute power.View full definition →: after data is loaded into your warehouse, dbt transforms it using SQLSQLSales Qualified Lead: a prospect the sales team has validated as ready for direct outreach and a proposal, having passed clear qualification criteria.View full definition →.
Before dbt, transformations were typically written as stored procedures, custom Python scripts, or embedded in ETLETLETL (Extract, Transform, Load) is a data integration process that pulls data from sources, reshapes it into a consistent format, and writes it into a target system.View full definition → tools. These approaches shared problems: no version control, no testing, no documentation, no lineage.
dbt solves all of these in one tool:
Knowledge check
1. In the ELT paradigm, where does dbt operate?
2. What is the primary purpose of the ref() function in dbt?
3. For a very large dataset where full rebuilds have become impractical, which materialization is most appropriate?
4. Select ALL problems with pre-dbt transformation approaches (stored procedures, custom scripts, ETL tools) that dbt was designed to solve.
Select all the correct answers.
5. Select ALL statements that correctly describe dbt testing and sources.
Select all the correct answers.
Models: A dbt model is a SQLSQLSales Qualified Lead: a prospect the sales team has validated as ready for direct outreach and a proposal, having passed clear qualification criteria.View full definition → file that defines a transformation. The file name becomes the table or view name in your warehouse. You write SELECT statements; dbt handles CREATE TABLE AS.
Refs: Models reference each other using the ref() function (e.g., FROM {{ ref('stg_customers') }}). This creates the dependency graph and allows dbt to run models in the right order.
Tests: Two types: generic tests (not_null, unique, accepted_values, relationships) and singular tests (custom SQLSQLSales Qualified Lead: a prospect the sales team has validated as ready for direct outreach and a proposal, having passed clear qualification criteria.View full definition → that should return zero rows if data is correct). Tests run after each build.
Sources: External tables (not built by dbt) referenced using source() function. dbt can test source freshness automatically.
Materializations: How dbt creates the output. Table (full rebuild), View (no data stored, query runs on access), Incremental (only processes new/changed records, critical for large datasets).
For large datasets, full refreshes become impractical. An incremental model processes only new records:
The pattern: filter the source to only records newer than the most recent record in the target table. On first run, process everything. On subsequent runs, process only the delta. This reduces computation cost from O(total rows) to O(new rows per run).
Incremental models require careful design: how do you handle late-arriving data? Updated records? Deletes? These edge cases are where dbt incremental model design gets complex, and where getting it wrong causes data qualitydata qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.View full definition → issues.
A mature dbt project follows a layer pattern:
This organization separates concerns: staging handles source-specific nuances, marts expose business logic, intermediate connects them.
At Gitlab (which open-sourced their entire dbt project), this structure lets any contributor understand where to look for any transformation, and prevents business logic from leaking into staging models.
1. Quelle est la principale innovation de dbt par rapport aux transformations SQLSQLSales Qualified Lead: a prospect the sales team has validated as ready for direct outreach and a proposal, having passed clear qualification criteria.View full definition → traditionnelles (stored procedures) ?
A) dbt est plus rapide
B) dbt ajoute version control, tests automatiques, documentation et lignage de données, les pratiques d'ingénierie logicielle appliquées aux transformations SQLSQLSales Qualified Lead: a prospect the sales team has validated as ready for direct outreach and a proposal, having passed clear qualification criteria.View full definition →
C) dbt supporte plus de bases de données
D) dbt génère automatiquement le SQLSQLSales Qualified Lead: a prospect the sales team has validated as ready for direct outreach and a proposal, having passed clear qualification criteria.View full definition →
Réponse: B
2. Qu'est-ce qu'une materialisation "incremental" dans dbt ?
A) Un modèle qui ne traite qu'un échantillon des données
B) Un modèle qui reconstruit la table entière à chaque run
C) Un modèle qui ne traite que les enregistrements nouveaux ou modifiés depuis le dernier run
D) Un modèle qui s'exécute automatiquement toutes les heures
Réponse: C
3. Dans l'organisation d'un projet dbt mature, que contient la couche "Staging" ?
A) Les tables dimensionnelles et de faits consommées par les outils BIBITechnologies and processes that turn raw data into actionable insights via reporting, dashboards and analysis, so teams can decide based on facts rather than intuition.View full definition →
B) La logique métier complexe et les jointures
C) Un modèle par table source avec transformation minimale, renommage, casting, sans logique métier
D) Les agrégations pour les dashboards exécutifs
Réponse: C
These actions are compiled in the role's Playbook.