Dbt
dbt is an open-source SQL-centric tool for defining, testing, documenting, and deploying modular data transformations and analytical data models.
DuckDB in Action
dbt is an SQL-centric transformation tool for building, testing, documenting, and deploying modular data models and pipelines.
- How it is used
- Builds tested, documented, modular transformations that move raw files into usable analytical outputs.
- How deep it goes
- Demonstrates project setup, sources, models, execution, inspection, tests, packages, and debugging, but not deployment details.
- Examples
- Uses ATP tennis CSV files, SQL models, schema.yml assertions, DuckDB inspection, and Parquet outputs.
- What you can do after
- Build it from scratch using dbt-duckdb to transform remote CSV files, test models, debug failures, and produce Parquet.
Architecting an Apache Iceberg Lakehouse
dbt is an open-source tool for transforming data in analytical data warehouses and lakehouses using SQL-based models, testing, and documentation.
- How it is used
- Keeps Iceberg maintenance close to transformation logic within analytics stacks already using dbt.
- How deep it goes
- Explains model, macro, and run-operation integration, but stops short of full scheduling and DAG management.
- Examples
- Demonstrates a transformation model, an expiration macro, and dbt run-operation execution.
- What you can do after
- Operate Iceberg post-transformation cleanup by defining dbt macros and invoking them with run-operation.
Read how Architecting an Apache Iceberg Lakehouse explains dbt →
Effective Data Analysis
dbt is an open-source tool that enables analytics engineers to transform data in a warehouse using SQL-based projects, testing, documentation, and software-development practices.
- How it is used
- Structures warehouse data collaboratively so analysts can create, layer, update, and augment tables using familiar SQL.
- How deep it goes
- Explains models through metadata, SQL, and Jinja references, but stops short of detailed warehouse implementation or advanced dbt features.
- Examples
- Uses product-team metrics, source-to-metrics layers, and a model file combining SQL, metadata, and Jinja.
- What you can do after
- Apply dbt to define warehouse tables for teams, organize data into layers, and reference related models or sources.
Related concepts
- analytics engineeringused by · Analytics engineering is the function of developing and maintaining transformed data models and analytical data infrastructure.
- Jinjauses · Jinja is a Python templating engine that generates text by rendering templates containing static content, expressions, variables, and control structures with supplied data.
- SQLuses · SQL is a standardized declarative language for defining, querying, and manipulating data and structures in relational database systems.
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