Rather than auditing architecture metrics retroactively, automated linting scripts can evaluate data models, check for active metadata tags, and confirm data privacy definitions before code hits a production environment. Real-World Open-Source Anchors
This article explores how to leverage the framework, how to find valuable resources on GitHub , and how to apply this knowledge to your work in data management. What is the DAMA-DMBOK? damadmbok pdf github work
What do you currently use? (e.g., dbt, Snowflake, Databricks, Airflow) What do you currently use
data-governance-hub/ │ ├── .github/ │ └── workflows/ │ ├── data-quality-ci.yml │ └── schema-validation.yml │ ├── dmbok-framework/ │ ├── 01_data_governance/ │ ├── 02_data_architecture/ │ └── 09_metadata_management/ │ ├── business-glossary/ │ ├── customer-domain.json │ └── finance-domain.json │ ├── schemas/ │ ├── analytics/ │ └── production/ │ └── README.md 1. Data Governance: The Hub and RACI Matrix While the full DMBOK2 PDF is copyrighted, many
: Navigate to GitHub and search for Damadmbok-workflow-docs .
While the full DMBOK2 PDF is copyrighted, many data professionals use GitHub to store complementary materials, implementation examples, and notes based on the DAMA framework. 3. Integrating DAMA-DMBOK with GitHub Work
Create a centralized repository named data-governance-hub . Use the root README.md to establish your Data Governance Council, outline roles, and map out a RACI matrix (Responsible, Accountable, Consulted, Informed) for your data domains. Use GitHub files to automatically route pull requests to the correct data steward based on the data domain being modified. 2. Metadata Management: Living Business Glossaries