Data Build Tool BigQuery

This tool enables data teams to design, validate, and optimize BigQuery transformation models using dbt-like workflows. It models SQL-based transformations, defines dependencies between models and sources, and captures tests and deployment configurations. By focusing on the specification, validation, and artifact generation rather than runtime execution, it helps teams plan changes, reason about lineage, and reduce lakehouse errors. Users include analytics engineers, data platform teams, and BI developers who iterate on data products and want repeatable, auditable pipelines. It supports standard BigQuery dialects and produces deployment-ready artifacts that fit modern CI/CD practices.
It embodies core logic such as dependency resolution, model compilation, schema tests, and environment-aware configurations. Required inputs include model definitions (name, sql, materialization), sources, and tests; optional advanced features cover incremental strategies, custom macros, and lineage exports. Outputs include compiled models, test results, and deployment configs. The tool validates inputs: non-empty names, syntactically valid SQL, and complete dependency graphs. It also checks for ambiguous references, missing sources, and circular dependencies. Outputs are structured and stable, enabling reproducible builds and traceable changes across environments.
Used during design, validation, and deployment preparation, it helps teams achieve faster delivery with fewer errors and clearer data lineage. The differentiator is a dbt-like workflow abstraction tailored for BigQuery, with strong typing on models and tests and artifact generation that maps directly to deployment tooling and CI/CD.