Comment pouvez-vous garantir la rétrocompatibilité dans un cadre de pipeline de données ?

One of the better aspects in using semantic versioning is it provides clear communication on sort of changes each version of the framework does. It also creates a “predictable” way for teams to understand how a version affects them. One of the issues I feel that is problematic in using SemVer though is that as the framework grows larger / complex, defining what is a major or minor change becomes much more difficult. Teams maintaining the framework must also ensure that there is rigorous documentation and disciplined code management.

Semantic versioning (SemVer) is a versioning scheme that communicates the nature of changes in software. It consists of three parts primarily : MAJOR, MINOR and PATCH. MAJOR: Increments when you make incompatible API changes. MINOR: Increments when you add functionality in a backward-compatible manner. PATCH: Increments when you make backward-compatible bug fixes. By adhering to Semantic Version, consumers of your data pipeline framework can easily understand the nature of changes and manage updates accordingly.

This principle states that software entities (classes, modules, functions) should be open for extension but closed for modification. Allow new functionalities to be added without modifying existing code. Avoid changing existing functionalities that would break backward compatibility. This can be achieved through the use of abstraction, design patterns like adapters or decorators, and clearly defined interfaces.

Adding versioning for schema is also useful because it’s a better way to communicate how a particular change will affect your code / pipelines. Another approach that I’ve found useful when it comes to schema evolution is introducing feature flags to introduce and test changes. This way teams have an option for backward compatibility while also being able to test new versions.

Data schema evolution allows changes to the structure of data while maintaining backward compatibility. Adding new fields or columns without breaking existing consumers. Renaming fields or columns in a way that doesn't disrupt existing processes. Handling changes in data types gracefully, ensuring compatibility with existing data. Techniques like versioning schema, using flexible data formats (JSON, Avro, Protocol Buffers), or employing schema registries can help manage schema evolution.

Comprehensive testing and documentation are crucial. Perform regression testing to ensure that changes don’t break existing functionalities. Test scenarios should cover backward compatibility checks. Clearly document changes, especially those that might impact backward compatibility. Provide guidelines for consumers on how to adapt to new versions.

Deprecation Strategy: Communicate clearly when certain functionalities or features will be phased out and provide ample time for consumers to migrate. Fallback Mechanisms: In cases where backward compatibility is difficult to maintain, consider providing fallback mechanisms or compatibility layers to support older versions. Community Engagement: Encourage communication and feedback from users and stakeholders. This helps in understanding their needs and addressing backward compatibility concerns effectively.