dbt and ELT Adoption Program Playbook
A program for adopting dbt-style ELT: modeling standards and medallion layering, tested and documented models in version control with CI, migration of legacy logic, and analyst enablement.
ELT with a transformation framework like dbt brings software engineering discipline to analytics: transformations live in version control, are tested, are documented, and ship through CI. This replaces the fragile stored procedures, hand-edited SQL scripts, and undocumented pipelines that accumulate in most data teams. This playbook adopts the practice as a program so the whole team modernizes together rather than one analyst at a time.
The architectural backbone is layering, commonly a medallion model: raw landing, cleaned and conformed staging, and business-ready marts. Clear layering makes models reusable and lineage understandable.
Phase-by-Phase
Assessment and Standards. Audit existing transformations, define naming and modeling standards, and design the layering so everyone builds consistently.
Foundation and CI. Set up the project structure, establish data and unit testing, and automate CI so every change is tested before it lands.
Migration of Logic. Migrate legacy transformation logic into version-controlled models, add data tests for freshness and integrity, and generate documentation automatically from the models.
Enablement and Scale. Train analysts to contribute, establish a code review workflow, and measure quality so the practice improves over time.
Team and Roles
Data engineers lead the foundation and migration. A data architect owns standards and layering. Analysts become contributors through enablement. QA helps define test coverage expectations and review gates.
Risks and Mitigations
Without standards, models drift into inconsistency, so define and enforce conventions early. Low test coverage undermines trust; make tests a merge requirement. Tribal knowledge is reduced through documentation-as-code and review.
Success Criteria
Target high model test coverage, strong documentation coverage, reduced time-to-change for transformations, and increased analyst autonomy.
Tooling
Use a transformation framework producing version-controlled SQL models, GitHub Actions for CI, and Grafana dashboards for quality metrics. Documentation and lineage are generated directly from the project.