great-expectations/great_expectations
Dec 2024 – Oct 2025
7 Months active
Languages Used
PythonJavaScriptMarkdownShellYAML
Technical Skills
Backend DevelopmentBug FixingTestingData QualityDocumentationRelease Management
Michael Shiplet
PROFILE
Michael Shiplet
Michael Shiplet contributed to the great-expectations/great_expectations repository by engineering robust data quality and validation features across multiple data sources, including Redshift, Snowflake, Databricks, and PostgreSQL. He implemented metrics such as column non-null counts and dynamic validation thresholds, expanded integration with SQLAlchemy, Pandas, and Spark, and improved test coverage and CI reliability. Using Python, SQL, and Docker, Michael enhanced release management, documentation, and code quality, streamlining onboarding and reducing configuration errors. His work addressed cross-engine consistency, enabled explicit schema selection, and improved data observability, reflecting a deep understanding of backend development, data engineering, and maintainable testing practices.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
16Total
Bugs
2
Commits
16
Features
9
Lines of code
3,141
Activity Months7
Your Network
40 peopleSame Organization
@greatexpectations.io13
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for great_expectations: Delivered Redshift Data Source Schema Support, enabling explicit Redshift schema selection in data source configuration. Implemented by adding schema_ to RedshiftConnectionDetails and updating the connection URL generation to include the specified schema. This improvement enhances configuration reliability in multi-schema Redshift deployments and reduces setup errors. The work was completed via a maintenance-focused commit: 906e4061c39567d1d1bfbf03cfeb425a4b6eeab9 (#11431).
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for great_expectations: Delivered Redshift Data Source Schema Support, enabling explicit Redshift schema selection in data source configuration. Implemented by adding schema_ to RedshiftConnectionDetails and updating the connection URL generation to include the specified schema. This improvement enhances configuration reliability in multi-schema Redshift deployments and reduces setup errors. The work was completed via a maintenance-focused commit: 906e4061c39567d1d1bfbf03cfeb425a4b6eeab9 (#11431).
October 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary: Delivered Great Expectations 1.5.11 with a new column.non_null_count metric, expanded metric support via MetricListMetricRetriever, and updated tests across column types along with release packaging changes (version bump and changelog entries). No major bugs reported this month. This work enhances data quality observability and validation capabilities, enabling teams to detect column-level data issues earlier and improve pipeline reliability. Notable commits include 77b32deb27ac0f1d0521826ab3973759bd58791d and c16f5491185b43a371ea8c54d2b1bc63ca8153d4 for release and metric-type updates.
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary: Delivered Great Expectations 1.5.11 with a new column.non_null_count metric, expanded metric support via MetricListMetricRetriever, and updated tests across column types along with release packaging changes (version bump and changelog entries). No major bugs reported this month. This work enhances data quality observability and validation capabilities, enabling teams to detect column-level data issues earlier and improve pipeline reliability. Notable commits include 77b32deb27ac0f1d0521826ab3973759bd58791d and c16f5491185b43a371ea8c54d2b1bc63ca8153d4 for release and metric-type updates.
July 2025
6 Commits • 3 Features
Jul 1, 2025July 2025 (2025-07) monthly summary for great_expectations/great_expectations. Focused on increasing test coverage, CI reliability, and release readiness for data sources and cloud snippets, with targeted improvements in code quality and documentation. Key outcomes include expanded test coverage for Snowflake and Databricks data sources, CI enhancement for Great Expectations Cloud snippets, and finalized release metadata/documentation updates to support version 1.5.4. These efforts reduce regression risk, accelerate PR feedback, and improve user-facing docs and data source consistency.
6 Commits • 3 Features
Jul 1, 2025July 2025 (2025-07) monthly summary for great_expectations/great_expectations. Focused on increasing test coverage, CI reliability, and release readiness for data sources and cloud snippets, with targeted improvements in code quality and documentation. Key outcomes include expanded test coverage for Snowflake and Databricks data sources, CI enhancement for Great Expectations Cloud snippets, and finalized release metadata/documentation updates to support version 1.5.4. These efforts reduce regression risk, accelerate PR feedback, and improve user-facing docs and data source consistency.
July 2025
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025: Delivered cross-engine data quality improvements in great-expectations/great_expectations. Implemented ColumnAggregateNonNullCount metric across SQLAlchemy, Pandas, and Spark with integration tests; standardized NonNullProportion renderer terminology from 'fraction' to 'proportion'; streamlined cloud E2E tests by removing the TableFactory fixture and replacing it with parameterized fixtures and explicit data source configurations. These changes enhance data quality visibility across engines, reduce test maintenance, and improve reliability of data quality signals for end users.
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025: Delivered cross-engine data quality improvements in great-expectations/great_expectations. Implemented ColumnAggregateNonNullCount metric across SQLAlchemy, Pandas, and Spark with integration tests; standardized NonNullProportion renderer terminology from 'fraction' to 'proportion'; streamlined cloud E2E tests by removing the TableFactory fixture and replacing it with parameterized fixtures and explicit data source configurations. These changes enhance data quality visibility across engines, reduce test maintenance, and improve reliability of data quality signals for end users.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 highlights: Delivered RedShift datasource integration for great_expectations, expanding supported data sources and enabling direct Redshift querying. Released version 1.3.13 with a dedicated changelog entry and updated documentation. Addressed related stability bugs and ensured a smooth rollout with release tagging. The work enhances data source coverage for enterprise workflows and reinforces reliability and developer experience.
1 Commits • 1 Features
Apr 1, 2025April 2025 highlights: Delivered RedShift datasource integration for great_expectations, expanding supported data sources and enabling direct Redshift querying. Released version 1.3.13 with a dedicated changelog entry and updated documentation. Addressed related stability bugs and ensured a smooth rollout with release tagging. The work enhances data source coverage for enterprise workflows and reinforces reliability and developer experience.
April 2025
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for great-expectations/great_expectations. Focused on enabling dynamic validation configuration and maintaining release quality. Highlights include delivery of a dynamic mostly parameter for ColumnMapExpectation with suite-parameter support, enhancements to type hints, and new unit tests; plus a controlled release process with version 1.3.2 and documentation updates. Overall, improvements reduce configuration friction for data validation, strengthen test coverage, and improve documentation for onboarding and downstream adoption.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for great-expectations/great_expectations. Focused on enabling dynamic validation configuration and maintaining release quality. Highlights include delivery of a dynamic mostly parameter for ColumnMapExpectation with suite-parameter support, enhancements to type hints, and new unit tests; plus a controlled release process with version 1.3.2 and documentation updates. Overall, improvements reduce configuration friction for data validation, strengthen test coverage, and improve documentation for onboarding and downstream adoption.
December 2024
1 Commits
Dec 1, 2024December 2024 monthly summary for great-expectations/great_expectations: Focused on stabilizing metric validation across batch assets. Delivered a bug fix that ensures the MetricRetriever re-initializes when the requested batch asset differs from the active asset, preventing stale validators from affecting metric retrieval. Implemented as part of the [BUGFIX] check for variance in validator and batch asset (#10744) with commit cf3490b2d84637cffdd265497464cfd39270abf0. Business impact: improved reliability of metric collection and validation across multiple data assets, reducing cross-asset failures during batch changes and increasing confidence in data quality dashboards. Demonstrated skills: debugging lifecycle of validators, batch asset handling, Python/Great Expectations internals, and Git-based collaboration.
1 Commits
Dec 1, 2024December 2024 monthly summary for great-expectations/great_expectations: Focused on stabilizing metric validation across batch assets. Delivered a bug fix that ensures the MetricRetriever re-initializes when the requested batch asset differs from the active asset, preventing stale validators from affecting metric retrieval. Implemented as part of the [BUGFIX] check for variance in validator and batch asset (#10744) with commit cf3490b2d84637cffdd265497464cfd39270abf0. Business impact: improved reliability of metric collection and validation across multiple data assets, reducing cross-asset failures during batch changes and increasing confidence in data quality dashboards. Demonstrated skills: debugging lifecycle of validators, batch asset handling, Python/Great Expectations internals, and Git-based collaboration.
December 2024
Activity
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Quality Metrics
Correctness95.6%
Maintainability95.0%
Architecture92.4%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaScriptMarkdownPythonShellYAML
Technical Skills
Backend DevelopmentBug FixingCI/CDCode QualityCode RefactoringData EngineeringData QualityData ValidationDatabase IntegrationDependency ManagementDevOpsDockerDocumentationIntegration TestingMaintenance
Repositories Contributed To
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