Implement Data Quality Frameworks
Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.
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Why it matters
Ensure reliable data pipelines by implementing robust data quality checks and validation strategies using Great Expectations, dbt tests, and data contracts.
Outcomes
What it gets done
Define and automate data quality expectations and contract rules.
Integrate validation into CI/CD pipelines and schedule regular checks.
Establish alerting, ownership, and remediation for data quality issues.
Monitor and report on data quality metrics.
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-data-quality-frameworks | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Overview
Data Quality Frameworks
Data Quality Frameworks delivers production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts. It guides you to identify critical datasets and quality dimensions, define expectations/tests and contract rules, automate validation in CI/CD and schedule checks, and set alerting, ownership, and remediation steps. Use this when implementing data quality checks in pipelines, setting up Great Expectations validation, building dbt test suites, establishing data contracts between teams, monitoring data quality metrics, or automating validation in CI/CD workflows.
What it does
Data Quality Frameworks offers production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.
When to use - and when NOT to
Use this skill when implementing data quality checks in pipelines, setting up Great Expectations validation, building comprehensive dbt test suites, establishing data contracts between teams, monitoring data quality metrics, or automating data validation in CI/CD workflows.
Do NOT use this skill when the data sources are undefined or unavailable, you cannot modify validation rules or schemas, or the task is unrelated to data quality or contracts.
Inputs and outputs
You provide critical datasets requiring validation, quality dimensions to monitor, and your pipeline architecture. The skill guides you to identify critical datasets and quality dimensions, define expectations/tests and contract rules, automate validation in CI/CD and schedule checks, and set alerting, ownership, and remediation steps. For detailed patterns, the skill references resources/implementation-playbook.md containing frameworks, templates, and examples.
Integrations
This skill covers Great Expectations, dbt tests, and CI/CD automation for data validation.
Who it's for
Data Quality Frameworks is for teams implementing data quality checks in pipelines, setting up Great Expectations validation, building dbt test suites, establishing data contracts between teams, monitoring data quality metrics, and automating data validation in CI/CD workflows.
FAQ
Common questions
Discussion
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