linkedin/openhouse
Dec 2024 – Dec 2024
1 Month active
Languages Used
JavaSQLScala
Technical Skills
Backend DevelopmentBig DataCloud StorageData EngineeringData WarehousingDatabase Management
vikram bohra
PROFILE
Vikram Bohra
Vikram Bohra contributed to the linkedin/openhouse project by enhancing data reliability and schema consistency across cloud and on-premises storage backends. He implemented cross-backend CTAS storage handling, enabling Create Table As Select operations to accurately determine table locations and storage types across S3 and HDFS, with added path validity checks for robust multi-storage support. Using Java and Scala, Vikram also addressed schema drift in Iceberg replica tables by preserving client-specified field IDs as table properties, ensuring consistent metadata reconstruction during schema evolution. His work demonstrated depth in backend development, distributed systems, and data engineering, directly improving data governance and operational correctness.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
915
Activity Months1
Work History
December 2024
2 Commits • 1 Features
Dec 1, 2024In December 2024, delivered two high-impact changes in the linkedin/openhouse project that improve reliability of data creation and consistency of schemas across storage backends. Cross-backend CTAS storage handling now correctly determines table locations and storage types across S3 and HDFS, with storage client path validity checks to ensure CTAS works reliably in multi-storage configurations. Additionally, Iceberg replica table schemas no longer reassign field IDs; original client-specified IDs are preserved as a table property and used to reconstruct metadata (partition specs and sort orders), preventing schema drift during evolution.
2 Commits • 1 Features
Dec 1, 2024In December 2024, delivered two high-impact changes in the linkedin/openhouse project that improve reliability of data creation and consistency of schemas across storage backends. Cross-backend CTAS storage handling now correctly determines table locations and storage types across S3 and HDFS, with storage client path validity checks to ensure CTAS works reliably in multi-storage configurations. Additionally, Iceberg replica table schemas no longer reassign field IDs; original client-specified IDs are preserved as a table property and used to reconstruct metadata (partition specs and sort orders), preventing schema drift during evolution.
December 2024
Activity
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Quality Metrics
Correctness95.0%
Maintainability80.0%
Architecture85.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaSQLScala
Technical Skills
Backend DevelopmentBig DataCloud StorageData EngineeringData WarehousingDatabase ManagementDistributed SystemsJava DevelopmentScala Development
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