How can you document and maintain ETL data mappings and transformations for transparent governance?

Documenting ETL data mappings and transformations is vital for transparency, accountability, and governance. Use tools like data dictionaries, metadata repositories, and workflow diagrams to document transformations, sources, and destinations. Maintain documentation through version control systems and regular updates to reflect changes accurately. Utilize standardized naming conventions and annotations for clarity. Ensure data lineage tracking to understand the flow of data from source to target. If on AWS; use Glue for metadata cataloging and Step Functions for workflow orchestration, Transparent governance ensures stakeholders understand data transformations, promoting trust and compliance.

Source-to-Target Mappings (STMs) are basically gold to data engineers. I’ve unfortunately seen many times where data teams do not keep this sort of information or what they have is completely outdated. Depending on the amount of tables that a team has may change the medium you store and update this information in. For teams with a smaller number of tables, excel is your friend here

Documente e mantenha os mapeamentos e transforma??es de dados do ETL para uma governan?a transparente, estabelecendo padr?es claros de documenta??o. Utilize um repositório centralizado de metadados para armazenar defini??es de origem/destino, regras de transforma??o e dependências. Implemente controle de vers?o para rastrear altera??es e promover responsabilidade. Crie diagramas abrangentes de linhagem de dados para visualizar o fluxo de dados. Desenvolva estruturas automatizadas de teste para valida??o. Reforce processos de gerenciamento de mudan?as, controles de acesso de usuários e auditorias regulares. Implemente registros e monitoramento para identifica??o proativa de problemas.

Documenting ETL mappings & transformations is akin to providing a map to a treasure. I recommend for Version Control storing such documents using Markdown with embedded diagrams via Mermaid on GITHub.

Documenting ETL data mappings serves multiple purposes. 1) It ensures transparency in data transformation; i.e. by documenting the mappings, stakeholders can easily understand how data flows from source to target destinations. 2) Proper documentation of ETL data mappings is also important (and in many industry sectors, mandatory) for regulatory compliance; provides a clear audit trail of how data is transformed and used. Also helps identify any potential risks or gaps in compliance. 3) Mapping documentation also facilitates data lineage analysis; i.e. helps trace data back to its original source and track its transformations, helping stakeholders validate its accuracy and reliability.

1) Use data mapping diagrams -> visual representations are often more effective at explaining data flow and transformations than verbose documentation. However, the dimensionality of the data may at times render this a difficult if not impractical exercise. 2) Use data mapping tables -> detailed tables documenting source-target mapping, including transformations. 3) Define and document data transformation rules -> document all logic and rules applied during transformations. 4. Store all metadata in a centralized repository to ensure a single source of truth.

1) Use version control -> track changes for history and convenient rollback if and when required. 2) Implement a process for reviewing modifications, i.e. have change management in place. Tooling (e.g. JIRA, Wrike, etc) is often a matter of organizational preference, so adds little value if discussed in this response. 3) Keep documentation current with process changes. 4) Work hard to cultivate good communication among all stakeholders involved in the above; strong alignment across the board is essential.

Data mappings should be governed. They should be in place before going into production. If you do them after, remember that it is not a documentation exercise because they need to be validated. Just because the data flows a certain way right now doesn't mean that's the way it's supposed to flow. They need to be used as part of testing to verify that you get the results expected. They need to be updated if the structure needs to change in production. They should have a regular review process to see if they need to be updated because you changed something in production and forgot to update them. They need to be stored where people can find them, authorized people can change them, and they can't be changed without accountability.