linkedin/openhouse
Nov 2025 – Apr 2026
6 Months active
Languages Used
GroovyJavaScalaSQL
Technical Skills
API designAPI developmentData EngineeringJavaSparkbackend development
Shantanu rawat
PROFILE
Shantanu Rawat
Over six months, this developer enhanced the linkedin/openhouse data platform by building and refining robust data engineering features using Java, Spark, and SQL. They centralized commit event metadata, improved Iceberg integration, and implemented partition-level statistics to enable granular analytics and reliable data lineage. Their work addressed complex schema challenges, such as ensuring completeness of nested column statistics and aligning metrics with Iceberg conventions. Through careful refactoring, comprehensive testing, and Docker-based validation, they improved data quality, auditability, and downstream analytics reliability. Their contributions emphasized maintainability, cross-engine compatibility, and data governance, consistently delivering solutions that strengthened OpenHouse’s backend data infrastructure.
Overall Statistics
Feature vs Bugs
64%Features
Repository Contributions
12Total
Bugs
4
Commits
12
Features
7
Lines of code
4,360
Activity Months6
Work History
April 2026
1 Commits
Apr 1, 2026April 2026 Monthly Summary — linkedin/openhouse This month focused on correcting data integrity for nested column statistics and strengthening the reliability of downstream analytics. A nested column statistics completeness fix was implemented to ensure all nested leaf fields are captured in partition stats, addressing a long-standing data loss issue in complex schemas. The change also improves future observability and dashboard accuracy by ensuring metrics map to the full, dot-separated field paths used by readable_metrics. Key initiatives and outcomes: - Delivered Nested Column Statistics Completeness Fix for linkedin/openhouse, guaranteeing inclusion of all nested leaf fields in partition stats collection. This eliminated silent drops for nested metrics and improved data fidelity in downstream analytics. - Implemented robust field indexing by full nested paths using TypeUtil.indexByName(schema.asStruct()) (replacing the previous usage of schema.columns()). This aligns with readable_metrics keys and ensures correct metric lookup for deeply nested schemas. - Code fix is backed by thorough testing: all 29 existing TableStatsCollectionSparkAppTest tests pass and all 25 TableStatsCollectorUtilTest tests pass. Strengthened testPartitionStatsWithNestedColumns() to verify presence of nested fields in nullCount and columnSizeInBytes. - Impact and value delivered: restored accuracy for nested metrics across the dataset, enabling reliable OpenHouse dashboards and analytics; quantified impact observed prior to fix was that ~44% of tracked OpenHouse tables had nested column stats dropped from published events. The fix removes this data loss path and provides consistent visibility of metrics such as user.name and user.age. - Technologies and skills demonstrated: Java/Scala Spark app, Iceberg schema handling, TypeUtil utilities for recursive field indexing, test-driven development, and clear documentation. The work was coordinated with team leads to ensure maintainability and ease of future enhancements. Commit reference: 794eca451613f822ed2097c1bdae97083710aecd Co-authored by: Shantanu Rawat, Claude Sonnet
1 Commits
Apr 1, 2026April 2026 Monthly Summary — linkedin/openhouse This month focused on correcting data integrity for nested column statistics and strengthening the reliability of downstream analytics. A nested column statistics completeness fix was implemented to ensure all nested leaf fields are captured in partition stats, addressing a long-standing data loss issue in complex schemas. The change also improves future observability and dashboard accuracy by ensuring metrics map to the full, dot-separated field paths used by readable_metrics. Key initiatives and outcomes: - Delivered Nested Column Statistics Completeness Fix for linkedin/openhouse, guaranteeing inclusion of all nested leaf fields in partition stats collection. This eliminated silent drops for nested metrics and improved data fidelity in downstream analytics. - Implemented robust field indexing by full nested paths using TypeUtil.indexByName(schema.asStruct()) (replacing the previous usage of schema.columns()). This aligns with readable_metrics keys and ensures correct metric lookup for deeply nested schemas. - Code fix is backed by thorough testing: all 29 existing TableStatsCollectionSparkAppTest tests pass and all 25 TableStatsCollectorUtilTest tests pass. Strengthened testPartitionStatsWithNestedColumns() to verify presence of nested fields in nullCount and columnSizeInBytes. - Impact and value delivered: restored accuracy for nested metrics across the dataset, enabling reliable OpenHouse dashboards and analytics; quantified impact observed prior to fix was that ~44% of tracked OpenHouse tables had nested column stats dropped from published events. The fix removes this data loss path and provides consistent visibility of metrics such as user.name and user.age. - Technologies and skills demonstrated: Java/Scala Spark app, Iceberg schema handling, TypeUtil utilities for recursive field indexing, test-driven development, and clear documentation. The work was coordinated with team leads to ensure maintainability and ease of future enhancements. Commit reference: 794eca451613f822ed2097c1bdae97083710aecd Co-authored by: Shantanu Rawat, Claude Sonnet
April 2026
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for linkedin/openhouse focusing on auditability and data quality improvements. Key outcomes include: 1) Audit Metadata Enrichment via Iceberg EnvironmentContext, enabling app-name to be included in audit events for filtering maintenance operations from user commits. 2) Robust Partition Statistics Emission, ensuring rowCount/columnCount are emitted for all partitions (including empty/deleted) and for tables without readable_metrics by switching to LEFT JOINs. Both initiatives included end-to-end testing across Spark versions and integration/unit tests, with no breaking OpenHouse API changes.
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for linkedin/openhouse focusing on auditability and data quality improvements. Key outcomes include: 1) Audit Metadata Enrichment via Iceberg EnvironmentContext, enabling app-name to be included in audit events for filtering maintenance operations from user commits. 2) Robust Partition Statistics Emission, ensuring rowCount/columnCount are emitted for all partitions (including empty/deleted) and for tables without readable_metrics by switching to LEFT JOINs. Both initiatives included end-to-end testing across Spark versions and integration/unit tests, with no breaking OpenHouse API changes.
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026: Delivered cross-engine traceability and data quality improvements for linkedin/openhouse. Implemented Trino query ID tracking in commit metadata with fallback logic to capture Trino query IDs and set commitAppName to 'trino' for Trino-based commits, enabling clean tracking alongside Spark. Fixed a critical bug where commit timestamps for partitioned tables were stored in seconds instead of milliseconds, introducing a conversion to milliseconds and validating with tests. Added test coverage for the bug fix and performed manual validation on a local docker setup for the feature.
2 Commits • 1 Features
Feb 1, 2026February 2026: Delivered cross-engine traceability and data quality improvements for linkedin/openhouse. Implemented Trino query ID tracking in commit metadata with fallback logic to capture Trino query IDs and set commitAppName to 'trino' for Trino-based commits, enabling clean tracking alongside Spark. Fixed a critical bug where commit timestamps for partitioned tables were stored in seconds instead of milliseconds, introducing a conversion to milliseconds and validating with tests. Added test coverage for the bug fix and performed manual validation on a local docker setup for the feature.
February 2026
January 2026
2 Commits • 2 Features
Jan 1, 2026Concise monthly summary for 2026-01 highlighting key features delivered, major fixes, and impact across the OpenHouse data platform. Focused on testability, data quality, and reliable analytics delivery to business stakeholders.
January 2026
2 Commits • 2 Features
Jan 1, 2026Concise monthly summary for 2026-01 highlighting key features delivered, major fixes, and impact across the OpenHouse data platform. Focused on testability, data quality, and reliable analytics delivery to business stakeholders.
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 (Month: 2025-12) delivered two high-impact updates for the OpenHouse data pipeline in linkedin/openhouse. A critical Shadow JAR relocation bug was fixed to preserve DataFrame schema integrity and prevent AnalysisException in the TABLE_STATS_COLLECTION Spark job. In addition, partition-level commit event collection was implemented for TableStats, publishing a new openhouseTableCommitEventsPartitions table to enable granular lineage and auditing across datasets. These changes improve reliability, observability, and business value while maintaining compatibility with existing pipelines.
2 Commits • 1 Features
Dec 1, 2025December 2025 (Month: 2025-12) delivered two high-impact updates for the OpenHouse data pipeline in linkedin/openhouse. A critical Shadow JAR relocation bug was fixed to preserve DataFrame schema integrity and prevent AnalysisException in the TABLE_STATS_COLLECTION Spark job. In addition, partition-level commit event collection was implemented for TableStats, publishing a new openhouseTableCommitEventsPartitions table to enable granular lineage and auditing across datasets. These changes improve reliability, observability, and business value while maintaining compatibility with existing pipelines.
December 2025
November 2025
3 Commits • 2 Features
Nov 1, 2025November 2025: Implemented centralized commit event metadata and Iceberg metadata enhancements for OpenHouse, delivering a single source of truth for commit events and app-name in snapshots; added automated daily population and upgraded Iceberg libraries to simplify downstream analytics. These changes improve data governance, consistency, and time-to-insight across OpenHouse datasets.
November 2025
3 Commits • 2 Features
Nov 1, 2025November 2025: Implemented centralized commit event metadata and Iceberg metadata enhancements for OpenHouse, delivering a single source of truth for commit events and app-name in snapshots; added automated daily population and upgraded Iceberg libraries to simplify downstream analytics. These changes improve data governance, consistency, and time-to-insight across OpenHouse datasets.
Activity
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Quality Metrics
Correctness96.6%
Maintainability83.4%
Architecture86.6%
Performance83.4%
AI Usage33.4%
Skills & Technologies
Programming Languages
GroovyJavaSQLScala
Technical Skills
API designAPI developmentApache SparkData EngineeringData ProcessingDataFrame OperationsDatabase ManagementGradleIcebergJavaSQLSparkTestingbackend developmentdata engineering
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