Energinet-DataHub/opengeh-python-packages
Jan 2025 – May 2025
3 Months active
Languages Used
MarkdownPythonYAMLTOML
Technical Skills
CI/CDData EngineeringPySparkPythonPython DevelopmentRelease Management
jmk-halfspace
PROFILE
Jmk-halfspace
Over three months, Jacob Kjøller developed and enhanced data engineering tools in the Energinet-DataHub/opengeh-python-packages repository. He built the Opengeh-pyspark package, introducing PySpark DataFrame utilities for timezone conversions and date manipulations, and integrated these with CI workflows to automate testing and release management. Jacob also added a seed method to the DatabricksApiClient, enabling SQL-based data seeding with robust error handling and method chaining, improving data pipeline reliability. Additionally, he implemented validation for project script executables in pyproject.toml, expanding test coverage and ensuring release readiness. His work leveraged Python, PySpark, and CI/CD best practices throughout.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
4
Lines of code
805
Activity Months3
Your Network
14 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for Energinet-DataHub/opengeh-python-packages focused on delivering a robust validation feature for project script executables defined in pyproject.toml, with enhanced test coverage and release readiness improvements.
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for Energinet-DataHub/opengeh-python-packages focused on delivering a robust validation feature for project script executables defined in pyproject.toml, with enhanced test coverage and release readiness improvements.
May 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 summary for Energinet-DataHub/opengeh-python-packages: Delivered a new seed functionality for the DatabricksApiClient to enable SQL-based data seeding with robust error handling and support for method chaining. The change includes a version bump and an adjustment to a platform-specific dependency marker in the lock file to ensure reproducibility and compatibility across Databricks environments. This work is anchored by commit d9e3dedc1025a1dcc03ab767f35cd0f8a79c1dda (featute: seed functionality on databricks api class (#102)).
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 summary for Energinet-DataHub/opengeh-python-packages: Delivered a new seed functionality for the DatabricksApiClient to enable SQL-based data seeding with robust error handling and support for method chaining. The change includes a version bump and an adjustment to a platform-specific dependency marker in the lock file to ensure reproducibility and compatibility across Databricks environments. This work is anchored by commit d9e3dedc1025a1dcc03ab767f35cd0f8a79c1dda (featute: seed functionality on databricks api class (#102)).
January 2025
4 Commits • 2 Features
Jan 1, 2025January 2025 performance summary for Energinet-DataHub/opengeh-python-packages: Delivered Opengeh-pyspark with PySpark DataFrame utilities (timezone conversions and date manipulations) and initial packaging, along with CI testing for the new package. Implemented PySpark functions release tooling and CI workflow integration, including release tag generation, inclusion in the release process, updated release notes, and test adjustments for timezone scenarios. These efforts improved data engineering capabilities for PySpark users, streamlined packaging and release processes, and strengthened CI reliability.
4 Commits • 2 Features
Jan 1, 2025January 2025 performance summary for Energinet-DataHub/opengeh-python-packages: Delivered Opengeh-pyspark with PySpark DataFrame utilities (timezone conversions and date manipulations) and initial packaging, along with CI testing for the new package. Implemented PySpark functions release tooling and CI workflow integration, including release tag generation, inclusion in the release process, updated release notes, and test adjustments for timezone scenarios. These efforts improved data engineering capabilities for PySpark users, streamlined packaging and release processes, and strengthened CI reliability.
January 2025
Activity
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Quality Metrics
Correctness85.8%
Maintainability85.8%
Architecture82.8%
Performance68.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonTOMLYAML
Technical Skills
API IntegrationCI/CDData EngineeringDatabase ManagementPackage ManagementPySparkPythonPython DevelopmentRelease ManagementTestingUnit Testing
Repositories Contributed To
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