How do you document and maintain your data mapping and schema transformation processes and rules?

1 Define your data mapping and schema transformation objectives

Before you start mapping and transforming your data, you need to define your objectives and requirements. What are the business goals and use cases of your data integration project? What are the data quality and performance criteria? What are the data governance and compliance standards? By clarifying your objectives, you can identify the scope, sources, targets, and rules of your data mapping and schema transformation processes.

2 Choose your data mapping and schema transformation tools

Depending on your data integration scenario, you may need different tools and methods to map and transform your data schemas and formats. ETL (extract, transform, load) tools are software applications that allow you to extract data from various sources, transform it according to your rules and logic, and load it into a target system or database. Data mapping tools are software applications that allow you to visually design, generate, and execute data mappings between source and target schemas and formats. SQL (structured query language) scripts are code snippets that allow you to query, manipulate, and transform data using a standard database language. XML (extensible markup language) or JSON (JavaScript object notation) transformations are code snippets that enable the transformation of data between XML or JSON formats using specific syntax and rules. All of these tools can be written manually or generated by ETL or data mapping tools.

3 Document your data mapping and schema transformation processes and rules

Documentation is an essential part of data mapping and schema transformation, as it helps to keep track of data integration logic, communicate with stakeholders, and maintain data quality and consistency. Documentation should include the names, types, locations, and characteristics of the data sources and targets involved in your data integration project; the structures, attributes, and values of the source and target systems; the logic, expressions, functions, and conditions that define how the data is mapped and transformed; visual representations of the data mappings and schema transformations that show the relationships between source and target schemas; as well as additional information that describes the mappings and transformations like date, author, version, purpose, and dependencies.

4 Test your data mapping and schema transformation processes and rules

Testing is an essential part of data mapping and schema transformation, ensuring accuracy, completeness, and quality of your data integration results. This process should include validation of the syntax, logic, and functionality of your data mappings and schema transformations, execution of your ETL or data mapping tools or SQL scripts, verification of the output compared to the expected output, and error handling to identify and resolve any issues that arise.

5 Maintain your data mapping and schema transformation processes and rules

Maintenance is an essential part of data mapping and schema transformation, helping to ensure processes and rules remain up to date, consistent, and reliable. This should include monitoring the performance, quality, and status of your data mappings and schema transformations using ETL or data mapping tools or SQL scripts. Additionally, you should audit your data mappings and schema transformations against your objectives, requirements, and standards using documentation, metadata, or SQL scripts. Finally, you should update your data mappings and schema transformations according to any changes in your data sources, targets, schemas, formats, rules, or objectives using ETL or data mapping tools or SQL scripts.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?