Data Testing
Testing Datasets for Schema Evolution
This prompt helps data science teams create test cases for handling schema changes in datasets over time. It focuses on ensuring backward compatibility, identifying breaking changes, and validating the integration of new fields or data structures.
Responsible:
Data Science
Accountable, Informed or Consulted:
Data Science, Engineering
THE PREP
Creating effective prompts involves tailoring them with detailed, relevant information and uploading documents that provide the best context. Prompts act as a framework to guide the response, but specificity and customization ensure the most accurate and helpful results. Use these prep tips to get the most out of this prompt:
Define the dataset’s current schema and planned changes, including added, removed, or modified fields.
Identify downstream applications or processes dependent on the schema.
Gather tools for managing and validating schema evolution, such as schema registries or automated testing frameworks.
THE PROMPT
Help create test cases for validating datasets when schema changes occur in [specific pipeline or data source, e.g., a customer database]. Focus on:
Schema Compatibility: Recommending checks, such as, ‘Test backward compatibility by validating that processes can handle older and newer schema versions simultaneously.’
New Field Integration: Suggesting validation, like, ‘Ensure new fields are correctly populated, and downstream applications recognize and process them without errors.’
Field Removal Impact: Including impact analysis, such as, ‘Test how the removal of fields affects workflows and identify processes that rely on deprecated columns.’
Data Type Changes: Proposing type validation, such as, ‘Validate that changes in field data types, like integers to floats or strings to enums, do not break existing logic.’
Version Tracking: Recommending validation steps, such as, ‘Implement schema version tracking and test that processes can identify and adapt to the correct schema version dynamically.’
Provide a detailed plan for testing schema evolution to ensure smooth integration and functionality of datasets in dynamic environments. If additional details about the schema changes or pipeline are needed, ask clarifying questions to refine the tests.
Bonus Add-On Prompts
Propose strategies for automating schema validation across multiple environments or systems.
Suggest methods for maintaining schema documentation to track changes and dependencies.
Highlight tools like Avro, JSON Schema, or Protobuf for managing and testing evolving schemas.
Use AI responsibly by verifying its outputs, as it may occasionally generate inaccurate or incomplete information. Treat AI as a tool to support your decision-making, ensuring human oversight and professional judgment for critical or sensitive use cases.
SUGGESTIONS TO IMPROVE
Focus on schema evolution in specific data storage formats, like Parquet or JSON.
Include tips for simulating schema changes in development or staging environments.
Propose ways to test schema evolution in streaming or real-time data pipelines.
Highlight tools like dbt or Apache Schema Registry for managing schema updates.
Add suggestions for documenting schema evolution to align with compliance or auditing needs.
WHEN TO USE
During schema updates in pipelines or databases with dependent applications.
To ensure compatibility when integrating new data sources or fields.
When transitioning systems to newer versions of data schemas.
WHEN NOT TO USE
For static schemas that do not change over time.
If schema documentation or expected changes are unclear or unavailable.