dbt data transformation : 2026

dbt = data build tool, transformation SQL avec testing + versioning + documentation. Standard 2026 modern data stack. Africa : adoption croissante.

TL;DR

- dbt = SQL transformations modulaires.

- Tests, docs, lineage automatic.

- dbt Core (free) vs dbt Cloud (paid).

- Compatible Snowflake, BigQuery, Databricks, Redshift, Postgres.

Pourquoi dbt

Avant dbt :

• SQL scripts isolés
• Pas de versioning
• Pas de tests
• Pas de lineage
• Documentation manquante

Avec dbt :

• Models = SELECT statements
• Macros (Jinja) = réutilisable
• Tests built-in (unique, not_null, accepted_values)
• Docs auto-générées
• DAG lineage visuel
• Git versioning natif

Stack dbt

• dbt Core :
• Open source CLI
• Free
• Self-hosted scheduling

• dbt Cloud :
• Managed IDE web
• Scheduler intégré
• $100-500/user/mois

Adapters :

• dbt-snowflake
• dbt-bigquery
• dbt-databricks
• dbt-redshift
• dbt-postgres
• dbt-clickhouse

Companion tools :

• dbt-utils (community macros)
• dbt-expectations (advanced tests)
• elementary (data observability)
• recce (PR diffs)

Project structure

`

my_project/

├── models/

│ ├── staging/ # raw data clean

│ ├── intermediate/ # business logic

│ └── marts/ # final tables

├── tests/

├── macros/

├── seeds/

├── snapshots/

└── dbt_project.yml

Naming convention :

• stg_source__entity (staging)
• int_entity (intermediate)
• fct_entity / dim_entity (marts)

`

Tests dbt

`yaml

# schema.yml

models:

• name: orders

columns:

• name: order_id

tests:

• unique
• not_null
• name: status

tests:

• accepted_values:

values: ['pending', 'paid', 'shipped']

• name: customer_id

tests:

• relationships:

to: ref('customers')

field: customer_id

Custom tests :

• dbt-expectations (great-expectations style)
• Singular tests SQL

`

FAQ

Q : dbt vs Apache Airflow ?

R : dbt = transformations data. Airflow = orchestration générale. Souvent ensemble.

Q : Coût Africa ?

R : dbt Core free + scheduling Airflow. Setup 2-4 semaines. Total < $100/mois infrastructure.

Conclusion

dbt data transformation 2026 : standard modern data stack. SQL modulaire + tests + docs + lineage = data engineering professionnel. Africa adoption croissante. Free Core ou $100-500 Cloud. ROI massif data quality.