Data Engineering Toolkit compiler
The det CLI turns the controlled V5.0.1 official-ODCS Excel superset into
canonical ODCS, DET execution metadata, and a readable dbt project. It includes
safe questionnaire-based workbook creation, ODCS/DDL/dbt-to-Excel import and
merge-safe synchronization, source-schema import, cross-sheet semantic/type
validation, atomic post-sync generation, Data Contract synchronization, SQLFluff,
and the isolated det prove acceptance flow. Data Contract CLI, dbt Core,
DuckDB, and SQLFluff are default dependencies.
V5.0.1 makes the visible official Quality sheet authoritative, preserves
advanced per-rule ODCS values, supports eight production warehouse providers, and adds mandatory
85.01% Python statement coverage. The generated dbt package has 98.53% DuckDB-scoped macro
implementation coverage plus 100% public-API direct execution coverage and supports dbt Core plus
Fusion-compatible projects. External resources
are isolated behind brokers; reusable services interpret workbooks and input
formats; independent components validate with stable diagnostic codes; and one
emitter owns each artifact family. The CLI remains a thin exposer over
ToolkitApplication.
Install
python -m pip install dbt-data-engineering-toolkit-compiler==5.0.1
The default installation includes Data Contract CLI, dbt Core, dbt-duckdb, DuckDB, and SQLFluff. Install one additional warehouse adapter per deployment environment, for example:
python -m pip install "dbt-data-engineering-toolkit-compiler[snowflake]"
Supported extras are athena, bigquery, clickhouse, databricks,
duckdb, postgres, redshift, snowflake, and spark.
Workflow
Use this sequence to move from workbook design to validated, testable dbt delivery.
| Step | Business action | Outcome | Command |
|---|---|---|---|
| 1 | Run the guided, safe workbook builder | Product basics; no demo rows by default | det workbook build PRODUCT.xlsx |
| 2 | Import or define target structures | ODCS model and property rows | det workbook import ... |
| 3 | Register/import upstream sources | Checked source fields and types | det source import ... |
| 4 | Define model inputs, mappings, and joins | Context-aware fields and executable cardinality checks | det workbook refresh PRODUCT.xlsx |
| 5 | Add contract rules on Quality; runtime handling on Operational Validation | ODCS tests, warnings, and quarantine with one clear authority | Use workbook sheets (no CLI command) |
| 6 | Validate the workbook | Actionable cross-sheet diagnostics | det validate PRODUCT.xlsx |
| 7 | Preview generated changes | No files changed | det generate PRODUCT.xlsx --project-dir dbt_product --dry-run --prune |
| Optional before step 8 | Override the default package source for a fork or mirror | dbt packages resolve from your published Git URL and pinned revision | export DBT_DATA_ENGINEERING_TOOLKIT_GIT_URL=https://github.com/systemizing-solutions/dbt_data_engineering_toolkit.git |
| 8 | Generate, sync, and lint | dbt packages resolve from the public Git URL and pinned revision | det generate ...; det check ... |
| 9 | Run isolated acceptance | Contract, dbt tests, evaluator | det prove PRODUCT.xlsx --project-dir dbt_product |
| 10 | Merge later schema changes | Mappings and rules preserved | det workbook sync ... |
| Demo | Opt in only when explicitly wanted | Multi-source customer training rows | det workbook build demo.xlsx --sample-customer-data |
Generated packages.yml uses the public repository by default. To use a fork or mirror, override its package source URL before running dbt deps:
export DBT_DATA_ENGINEERING_TOOLKIT_GIT_URL=https://github.com/systemizing-solutions/dbt_data_engineering_toolkit.git
If you need a custom variable name, set Toolkit Git Env on the DET Build sheet
before det generate, then export that variable name in your shell.
Quick start
det new customer_360
cd customer_360
det prove data_product.xlsx --project-dir .
This starter mode creates and validates a populated workbook, generates a complete DuckDB dbt project, and proves it through the normal contract and dbt pipeline. The public package URL is the default; DBT_DATA_ENGINEERING_TOOLKIT_GIT_URL remains available for forks and mirrors.
For production-safe blank workbooks, use det workbook build data_product.xlsx --no-input. The starter and governed paths use the same compiler and proof services.
Continue with det --help and det workbook --help. The source repository
root README contains the complete workbook-to-ODCS-to-dbt walkthrough,
architecture, compatibility matrix, testing definitions, and deployment guide.
Metadata
Release files for dbt-data-engineering-toolkit-compiler 5.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dbt_data_engineering_toolkit_compiler-5.0.1.tar.gz | 278.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dbt_data_engineering_toolkit_compiler-5.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 579.4 kB
Release files / dbt_data_engineering_toolkit_compiler-5.0.1.tar.gz
| Download URL | dbt_data_engineering_toolkit_compiler-5.0.1.tar.gz |
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| Size | 278.3 kB |
| Tags | Source |
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| Tags | Python 3 |
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| Uploaded via |
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