apache/iceberg-go
Apr 2026 – May 2026
2 Months active
Languages Used
GoMarkdownJSONMakefilePythonYAML
Technical Skills
API designAPI developmentBloom filtersCLI DevelopmentData ProcessingGo
Tanmay Rauth
PROFILE
Tanmay Rauth
Contributed to the apache/iceberg-go repository by building and enhancing core data management features, focusing on stability, performance, and compatibility. Developed Hadoop catalog support with robust namespace and table operations, integrated CLI tooling, and implemented version-hint management to streamline metadata workflows. Improved cloud storage integration by addressing S3 region handling and enabling batch deletions, while introducing Zstandard compression for efficient metadata storage. Enhanced reliability through Spark-based integration testing and concurrency-safe JSON schema handling. Leveraged Go and Python for backend development, data processing, and testing, delivering twelve features and eight bug fixes over two months to strengthen production readiness and maintainability.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
26Total
Bugs
8
Commits
26
Features
12
Lines of code
7,001
Activity Months2
Your Network
641 peopleSame Organization
@apple.com598
Work History
May 2026
12 Commits • 5 Features
May 1, 2026May 2026 key accomplishments for apache/iceberg-go: Key features delivered: - Hadoop Catalog Core and Namespace/Table management with Iceberg CLI and version-hint management: core CRUD (CreateTable/LoadTable/CheckTableExists), namespace operations, table listing/renaming/dropping, and CLI integration with version-hint support. Commits include 69fc3cdc5b745971b7f77a9039a91bee83ddfb85, 440647e26420dffac3f8d89155cee23c6094805b, 5675b8cfeed542802ca6142ab36fec2abba0603c, 9d8ec298d32914007917df0c1599b1c059174f82, and f026751eee9710275b6e25590a3388d1e816fcc4. - Hadoop namespace and table catalog operations: CreateNamespace, DropNamespace, ListNamespaces, ListTables, DropTable, and related property handling. Commits: 440647e26420dffac3f8d89155cee23c6094805b and 5675b8cfeed542802ca6142ab36fec2abba0603c. - Spark integration testing framework for Hadoop catalog: Docker-based Spark infra with hadoop validation tests enabling cross-compatibility testing against Spark's HadoopCatalog. Commit: 330fcdf6c371ce2e3d469badf8ef2e8861fdcfca. - Zstandard (zstd) compression for metadata: added support and wiring for read/write paths (compression codec zstd). Commit: c62af3c624bcf7afd9f75123fbc6ce60362f8da8. - Data race fix in Schema MarshalJSON: stack-local copy of IdentifierFieldIDs to avoid concurrent access during manifest writing. Commit: 5f3462960491261397a1f01b82ee5b1f7ba2b378. - S3 301 redirect fix with SigV4 region and virtual-host addressing: propagate signing region and switch to virtual-hosted style addressing for table IO. Commit: f6cd9c411f7ad92372c24d0eea49dfb66a29a7f6. Major bugs fixed: - Data race in MarshalJSON during manifest writing (commit 5f3462960…). - S3 PermanentRedirect issues corrected by propagating SigV4 region and enabling virtual-hosted addressing (commit f6cd9c411…). Overall impact and accomplishments: - Strengthened production-readiness of the Hadoop catalog with robust CRUD/namespace operations and CLI integration, improving operational efficiency and accuracy of metadata management. - Expanded testing coverage and reliability through Spark integration tests and v3 metadata test support, reducing regression risk for production workloads. - Improved metadata handling efficiency and safety via Zstandard compression and a data-race-free MarshalJSON implementation, lowering latency and increasing stability. - Hardened cloud storage reliability for S3 I/O with correct region handling and addressing, reducing write-time errors and redirects. Technologies and skills demonstrated: - Go, filesystem operations, and CLI integration for catalog management. - Spark-based integration testing, Docker, and validation scripting. - Metadata compression (Zstandard) and JSON schema validation patterns. - Concurrency safety, testing discipline, and cloud IO (S3) resilience.
12 Commits • 5 Features
May 1, 2026May 2026 key accomplishments for apache/iceberg-go: Key features delivered: - Hadoop Catalog Core and Namespace/Table management with Iceberg CLI and version-hint management: core CRUD (CreateTable/LoadTable/CheckTableExists), namespace operations, table listing/renaming/dropping, and CLI integration with version-hint support. Commits include 69fc3cdc5b745971b7f77a9039a91bee83ddfb85, 440647e26420dffac3f8d89155cee23c6094805b, 5675b8cfeed542802ca6142ab36fec2abba0603c, 9d8ec298d32914007917df0c1599b1c059174f82, and f026751eee9710275b6e25590a3388d1e816fcc4. - Hadoop namespace and table catalog operations: CreateNamespace, DropNamespace, ListNamespaces, ListTables, DropTable, and related property handling. Commits: 440647e26420dffac3f8d89155cee23c6094805b and 5675b8cfeed542802ca6142ab36fec2abba0603c. - Spark integration testing framework for Hadoop catalog: Docker-based Spark infra with hadoop validation tests enabling cross-compatibility testing against Spark's HadoopCatalog. Commit: 330fcdf6c371ce2e3d469badf8ef2e8861fdcfca. - Zstandard (zstd) compression for metadata: added support and wiring for read/write paths (compression codec zstd). Commit: c62af3c624bcf7afd9f75123fbc6ce60362f8da8. - Data race fix in Schema MarshalJSON: stack-local copy of IdentifierFieldIDs to avoid concurrent access during manifest writing. Commit: 5f3462960491261397a1f01b82ee5b1f7ba2b378. - S3 301 redirect fix with SigV4 region and virtual-host addressing: propagate signing region and switch to virtual-hosted style addressing for table IO. Commit: f6cd9c411f7ad92372c24d0eea49dfb66a29a7f6. Major bugs fixed: - Data race in MarshalJSON during manifest writing (commit 5f3462960…). - S3 PermanentRedirect issues corrected by propagating SigV4 region and enabling virtual-hosted addressing (commit f6cd9c411…). Overall impact and accomplishments: - Strengthened production-readiness of the Hadoop catalog with robust CRUD/namespace operations and CLI integration, improving operational efficiency and accuracy of metadata management. - Expanded testing coverage and reliability through Spark integration tests and v3 metadata test support, reducing regression risk for production workloads. - Improved metadata handling efficiency and safety via Zstandard compression and a data-race-free MarshalJSON implementation, lowering latency and increasing stability. - Hardened cloud storage reliability for S3 I/O with correct region handling and addressing, reducing write-time errors and redirects. Technologies and skills demonstrated: - Go, filesystem operations, and CLI integration for catalog management. - Spark-based integration testing, Docker, and validation scripting. - Metadata compression (Zstandard) and JSON schema validation patterns. - Concurrency safety, testing discipline, and cloud IO (S3) resilience.
May 2026
April 2026
14 Commits • 7 Features
Apr 1, 2026Month: 2026-04 — Concise monthly summary focused on delivering business value, stability, and performance improvements for the Iceberg Go repository. The team advanced core data-path capabilities, expanded catalog support, and strengthened reliability and developer ergonomics through targeted features, compatibility fixes, and safety improvements.
April 2026
14 Commits • 7 Features
Apr 1, 2026Month: 2026-04 — Concise monthly summary focused on delivering business value, stability, and performance improvements for the Iceberg Go repository. The team advanced core data-path capabilities, expanded catalog support, and strengthened reliability and developer ergonomics through targeted features, compatibility fixes, and safety improvements.
Activity
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Quality Metrics
Correctness95.4%
Maintainability83.8%
Architecture90.0%
Performance84.6%
AI Usage24.6%
Skills & Technologies
Programming Languages
GoJSONMakefileMarkdownPythonYAML
Technical Skills
API designAPI developmentAWS S3Bloom filtersCLI DevelopmentCLI developmentData ProcessingDockerGoGo programmingIntegration TestingJSON handlingParquetPythonSchema Management
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