linkedin/datahub-gma
Feb 2025 – Aug 2025
6 Months active
Languages Used
JavaSQLPDL
Technical Skills
API DevelopmentBackend DevelopmentDatabase ManagementJavaData AuditingSQL
Pallavi Kotkar
PROFILE
Pallavi Kotkar
Pallavi Kotkar developed and enhanced core data management features in the linkedin/datahub-gma repository over six months, focusing on robust asset creation, data lifecycle management, and validation reliability. She implemented APIs for asset creation with duplicate handling, introduced both permanent and soft deletion mechanisms to support regulatory compliance and auditability, and improved data access logic to filter active records using SQL and Java. Her work included strengthening data validation in the DAO layer with targeted unit testing, ensuring data integrity across ingestion pipelines. Throughout, Pallavi applied backend development, database management, and data modeling skills to deliver maintainable, testable solutions.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
8
Lines of code
1,488
Activity Months6
Your Network
12 people
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025: Focused on strengthening data validation reliability in the DAO layer of linkedin/datahub-gma by adding unit tests for validateAgainstSchemaAndFillinDefault. This work ensures default values and optional fields in nested data structures are correctly handled, reducing risk of data integrity issues and regression in ingestion pipelines. No major bugs were fixed this month. The change improves test coverage and confidence in data persistence.
1 Commits • 1 Features
Aug 1, 2025August 2025: Focused on strengthening data validation reliability in the DAO layer of linkedin/datahub-gma by adding unit tests for validateAgainstSchemaAndFillinDefault. This work ensures default values and optional fields in nested data structures are correctly handled, reducing risk of data integrity issues and regression in ingestion pipelines. No major bugs were fixed this month. The change improves test coverage and confidence in data persistence.
August 2025
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for linkedin/datahub-gma: Implemented Soft Delete with Historical Data in the Data Access Layer, enabling deleted entities to be marked via deleted_ts across create, update, and delete operations. Preserves historical data for auditability and potential recovery. Updated unit tests and documentation to align with behavior. Commits include: 8f17cedaaeddf2bf8fd68e5777e79ed4a4aa942f (Updating method for create(), update() and delete() to handle soft delete by setting deleted_ts (#549)).
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for linkedin/datahub-gma: Implemented Soft Delete with Historical Data in the Data Access Layer, enabling deleted entities to be marked via deleted_ts across create, update, and delete operations. Preserves historical data for auditability and potential recovery. Updated unit tests and documentation to align with behavior. Commits include: 8f17cedaaeddf2bf8fd68e5777e79ed4a4aa942f (Updating method for create(), update() and delete() to handle soft delete by setting deleted_ts (#549)).
June 2025
2 Commits • 1 Features
Jun 1, 2025For 2025-06, the developer contributed a key feature delivering Soft Deletion Support and Active Records Filtering in linkedin/datahub-gma. This work introduces soft deletion checks across data access, filters on deleted_ts to exclude soft-deleted records, and updates to the get/batchGet APIs and underlying SQL generation. Tests were updated accordingly. No other major bug fixes were required this month; the focus was on data integrity and API correctness.
2 Commits • 1 Features
Jun 1, 2025For 2025-06, the developer contributed a key feature delivering Soft Deletion Support and Active Records Filtering in linkedin/datahub-gma. This work introduces soft deletion checks across data access, filters on deleted_ts to exclude soft-deleted records, and updates to the get/batchGet APIs and underlying SQL generation. Tests were updated accordingly. No other major bug fixes were required this month; the focus was on data integrity and API correctness.
June 2025
April 2025
4 Commits • 2 Features
Apr 1, 2025April 2025 - linkedin/datahub-gma: Implemented a robust data lifecycle improvement by delivering permanent deletion for entities and all related aspects, adding DELETE_ALL event handling, new deletion methods, and comprehensive unit tests. Fixed the DeleteAll flow by using getLatest for record retrieval and correcting SQL to ensure accurate deletions across configurations. Introduced safeguards to verify aspect presence before applying delete results. Improved type-safety in BaseLocalDAO by replacing nullable parameters with Optional.empty(), signaling absent values more clearly. These changes enhance data governance, regulatory readiness, and system reliability, backed by expanded test coverage.
April 2025
4 Commits • 2 Features
Apr 1, 2025April 2025 - linkedin/datahub-gma: Implemented a robust data lifecycle improvement by delivering permanent deletion for entities and all related aspects, adding DELETE_ALL event handling, new deletion methods, and comprehensive unit tests. Fixed the DeleteAll flow by using getLatest for record retrieval and correcting SQL to ensure accurate deletions across configurations. Introduced safeguards to verify aspect presence before applying delete results. Improved type-safety in BaseLocalDAO by replacing nullable parameters with Optional.empty(), signaling absent values more clearly. These changes enhance data governance, regulatory readiness, and system reliability, backed by expanded test coverage.
March 2025
2 Commits • 2 Features
Mar 1, 2025Monthly summary for 2025-03 (linkedin/datahub-gma): Delivered two core features that advance data governance and observability, with corresponding test updates and clean commits. Key value: precise URN generation across paths and robust auditing for new aspects, enabling better lineage, traceability, and compliance. Impact and scope: - Focused feature work on URN-aware SQL generation and MAE emission, improving data accuracy and auditability. - Strengthened testing coverage to validate both URN creation paths and MAE production, reducing regression risk. - No major defects reported this month; efforts concentrated on reliable feature delivery and maintainability. Technologies/skills demonstrated: - Conditional SQL generation logic and feature-flag-driven behavior - Metadata Audit Event (MAE) emission integration for data changes - Test-driven development with targeted test updates for correctness and auditability Repository: linkedin/datahub-gma
2 Commits • 2 Features
Mar 1, 2025Monthly summary for 2025-03 (linkedin/datahub-gma): Delivered two core features that advance data governance and observability, with corresponding test updates and clean commits. Key value: precise URN generation across paths and robust auditing for new aspects, enabling better lineage, traceability, and compliance. Impact and scope: - Focused feature work on URN-aware SQL generation and MAE emission, improving data accuracy and auditability. - Strengthened testing coverage to validate both URN creation paths and MAE production, reducing regression risk. - No major defects reported this month; efforts concentrated on reliable feature delivery and maintainability. Technologies/skills demonstrated: - Conditional SQL generation logic and feature-flag-driven behavior - Metadata Audit Event (MAE) emission integration for data changes - Test-driven development with targeted test updates for correctness and auditability Repository: linkedin/datahub-gma
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a robust Asset Creation API in linkedin/datahub-gma with duplicate handling and multi-aspect processing, enhancing data integrity and processing capabilities. Implemented pre-processing of aspects, multi-aspect persistence, and transaction-managed error handling to support reliable asset creation.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a robust Asset Creation API in linkedin/datahub-gma with duplicate handling and multi-aspect processing, enhancing data integrity and processing capabilities. Implemented pre-processing of aspects, multi-aspect persistence, and transaction-managed error handling to support reliable asset creation.
Activity
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Quality Metrics
Correctness91.0%
Maintainability83.6%
Architecture83.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaPDLSQL
Technical Skills
API DevelopmentBackend DevelopmentData AuditingData ModelingDatabase ManagementJavaJava DevelopmentSQLUnit Testing
Repositories Contributed To
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