linkedin/datahub-gma
Mar 2026 – May 2026
2 Months active
Languages Used
Java
Technical Skills
JavaSQLbackend developmentdatabase managementEbean ORM
Natallia Ulashchick
PROFILE
Natallia Ulashchick
Worked on the linkedin/datahub-gma repository to deliver two backend features focused on database efficiency and maintainability. Developed batch deletion support in the DAO for stale metadata, enabling bulk soft-deletes with only two database round-trips per batch and integrating robust SQL templates, parsing utilities, and observability through InstrumentedEbeanLocalAccess. Later, implemented a targeted performance optimization by introducing an optional FORCE INDEX hint for offset-pagination queries, allowing configurable index usage and reducing unnecessary full-table scans. Both features were built using Java, SQL, and Ebean ORM, with extensive integration testing to ensure safe archival, improved data hygiene, and consistent query performance.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
67,792
Activity Months2
Your Network
109 peopleSame Organization
@linkedin.com96
Allen AverbukhMember
Xin(Adam) ChenMember
Ahmet Alp BalkanMember
Alex HopmannMember
Alan Z.Member
Aman GuptaMember
Amaan MarfatiaMember
AMAN PANDEYMember
Abhishek NathMember
Shared Repositories
13
Work History
May 2026
2 Commits • 1 Features
May 1, 2026May 2026 for linkedin/datahub-gma: Implemented a targeted performance optimization in the Ebean DAO for offset-pagination by introducing an optional FORCE INDEX hint with validation. Extended the configureOptionalForceIndex API to accept both aspect and URN classes, enabling flexible index-criteria mapping. The change propagates via InstrumentedEbeanLocalAccess to downstream components (e.g., metadata-graph-assets for mlmodelinstance), ensuring consistent index-choice overrides. Result: reduced latency and avoided unnecessary full-table scans on small LIMITs, with safer, configurable index usage that supports downstream tooling.
2 Commits • 1 Features
May 1, 2026May 2026 for linkedin/datahub-gma: Implemented a targeted performance optimization in the Ebean DAO for offset-pagination by introducing an optional FORCE INDEX hint with validation. Extended the configureOptionalForceIndex API to accept both aspect and URN classes, enabling flexible index-criteria mapping. The change propagates via InstrumentedEbeanLocalAccess to downstream components (e.g., metadata-graph-assets for mlmodelinstance), ensuring consistent index-choice overrides. Result: reduced latency and avoided unnecessary full-table scans on small LIMITs, with safer, configurable index usage that supports downstream tooling.
May 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 (linkedin/datahub-gma): Delivered Batch Deletion Support in DAO for Stale Metadata, enabling bulk soft-deletes with exactly two database round-trips per batch, significantly reducing cleanup overhead and improving data hygiene. Implemented the underlying data model and DAO enhancements, including readDeletionInfoBatch and batchSoftDeleteAssets, with defense-in-depth criteria to ensure safe archival and removal operations. Integrated SQL templates and parsing utilities (SQLStatementUtils, EBeanDAOUtils) to support batch operations and ensured observability through InstrumentedEbeanLocalAccess.
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 (linkedin/datahub-gma): Delivered Batch Deletion Support in DAO for Stale Metadata, enabling bulk soft-deletes with exactly two database round-trips per batch, significantly reducing cleanup overhead and improving data hygiene. Implemented the underlying data model and DAO enhancements, including readDeletionInfoBatch and batchSoftDeleteAssets, with defense-in-depth criteria to ensure safe archival and removal operations. Integrated SQL templates and parsing utilities (SQLStatementUtils, EBeanDAOUtils) to support batch operations and ensured observability through InstrumentedEbeanLocalAccess.
Activity
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Quality Metrics
Correctness93.4%
Maintainability86.6%
Architecture93.4%
Performance86.6%
AI Usage46.6%
Skills & Technologies
Programming Languages
Java
Technical Skills
Ebean ORMJavaSQLbackend developmentdatabase management
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