goldmansachs/legend-engine
Oct 2024 – Apr 2025
2 Months active
Languages Used
Java
Technical Skills
Backend DevelopmentJava DevelopmentPersistenceData EngineeringPersistence Frameworks
prasar-ashutosh
PROFILE
Prasar-ashutosh
Ashutosh Prasar contributed backend and data engineering features to the goldmansachs/legend-engine repository, focusing on extensible dataset metadata and partition management. He enhanced the DatasetReference model in Java to support optional properties, enabling richer metadata capture for ICEBERG-origin datasets while maintaining backward compatibility. In a separate effort, Ashutosh implemented partition deletion support in the persistence layer, introducing a delete_partition dataset type and updating the ingestion pipeline for robust partition-level data cleanup. His work involved Java development, persistence frameworks, and test coverage expansion, resulting in improved data governance, lifecycle management, and extensibility for complex data workflows within the platform.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
815
Activity Months2
Your Network
36 people
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Implemented Partition Deletion Support in the Persistence Layer for goldmansachs/legend-engine, enabling delete_partition dataset type and end-to-end lifecycle for partition deletions. The work touched the persistence API, dataset conversion, and ingestion path, culminating in a robust approach to partition-level data cleanup and governance. Outcomes include improved data hygiene, simpler retention policy enforcement, and a platform that supports more flexible data management.
1 Commits • 1 Features
Apr 1, 2025April 2025: Implemented Partition Deletion Support in the Persistence Layer for goldmansachs/legend-engine, enabling delete_partition dataset type and end-to-end lifecycle for partition deletions. The work touched the persistence API, dataset conversion, and ingestion path, culminating in a robust approach to partition-level data cleanup and governance. Outcomes include improved data hygiene, simpler retention policy enforcement, and a platform that supports more flexible data management.
April 2025
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024: Implemented optional additional properties in DatasetReference for legend-engine to support ICEBERG as a table origin, and expanded test coverage to validate their presence. The change was implemented in DatasetReferenceImplAbstract and associated tests, ensuring metadata richness without breaking existing behavior. Business value: enhances data lineage and governance capabilities by capturing dataset-level properties, supports ICEBERG-origin scenarios, and reduces manual configuration gaps for downstream pipelines. This lays groundwork for future extensibility of dataset metadata. Technical impact: Java-based model changes in DatasetReferenceImplAbstract, updated tests to cover new properties, and a targeted commit ensuring correctness and maintainability. Commit: e10730153f00b55b6a582bea4d0a5a5e03db4709 ("Fix dataset Reference to have dataset optional properties (#3213)").
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024: Implemented optional additional properties in DatasetReference for legend-engine to support ICEBERG as a table origin, and expanded test coverage to validate their presence. The change was implemented in DatasetReferenceImplAbstract and associated tests, ensuring metadata richness without breaking existing behavior. Business value: enhances data lineage and governance capabilities by capturing dataset-level properties, supports ICEBERG-origin scenarios, and reduces manual configuration gaps for downstream pipelines. This lays groundwork for future extensibility of dataset metadata. Technical impact: Java-based model changes in DatasetReferenceImplAbstract, updated tests to cover new properties, and a targeted commit ensuring correctness and maintainability. Commit: e10730153f00b55b6a582bea4d0a5a5e03db4709 ("Fix dataset Reference to have dataset optional properties (#3213)").
Activity
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Quality Metrics
Correctness85.0%
Maintainability85.0%
Architecture85.0%
Performance70.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Java
Technical Skills
Backend DevelopmentData EngineeringJava DevelopmentPersistencePersistence Frameworks
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