NYPL/drb-etl-pipeline
Oct 2024 – Apr 2025
7 Months active
Languages Used
PythonSQLMarkdownMakefile
Technical Skills
AWS S3Cloud ComputingData EngineeringETLPythonAPI Integration
Dmitri Slory
PROFILE
Dmitri Slory
Dmitri Slory developed and maintained the NYPL/drb-etl-pipeline, delivering robust data ingestion, analytics, and catalog management features over seven months. He engineered scalable ETL workflows integrating sources like Airtable and CLACSO, standardized ingestion with a unified RecordIngestor, and automated manifest generation and storage using AWS S3. His work included refactoring for maintainability, implementing deletion and cleanup routines to ensure data consistency, and enhancing analytics by aggregating usage across storage buckets. Using Python, SQL, and cloud services, Dmitri focused on code readability, test coverage, and modular design, resulting in a maintainable, extensible pipeline that improved data quality and operational efficiency.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
33Total
Bugs
0
Commits
33
Features
18
Lines of code
55,994
Activity Months7
Your Network
18 people
Work History
April 2025
8 Commits • 3 Features
Apr 1, 2025April 2025 — NYPL/drb-etl-pipeline: Key business outcomes and technical milestones. Summary: In April 2025, the drb-etl-pipeline delivered three core capabilities across Schomburg data access, collection orchestration, and ingestion scalability. 1) Public access to Schomburg PDFs in S3 was enabled by applying a public-read ACL on copy operations and by improving error logging for missing PDFs, reducing data access friction and improving reliability. 2) A Schomburg Collection creation script was added to automatically assemble collections by querying SCH Collection/Hathi files with cluster_status=true, collecting edition IDs, and POSTing to a local API with title/creator/description and edition IDs, enabling faster cataloging and richer metadata. 3) The Unified RecordIngestor framework was introduced to standardize ingestion across MET, HathiTrust, NYPL, LOC, DOAB, and MUSE, decoupling services and enabling a consistent, scalable ingestion flow. These efforts collectively improve data availability, quality, and operational efficiency, and lay groundwork for future expansion. Key commits and traceability: - Public PDFs in S3: 53eaa33d4317e94a2644426040dfe8d7d75bd906 - Schomburg Collection script: 60a72359cc6b61ed6348d26d14eaf2436dcfc97d - Unified RecordIngestor commits across MET/HathiTrust/NYPL/LOC/DOAB/MUSE: 59a9cb658d61edcbd25344e96cc219d71b4b63d6; 3f21aef8eb83b8cf2cb74f79ffac17693974032a; 0512d718950bb38ecb14db8b0bc01f941dae5d1a; 371c8d97a189fbbdc3ec2b59b2af53ac79d1134e; 26af1e1fd6920cc68c4a97836fd53fd6c27ab476; 0356de65a171e1431a6daac80205b3873feeed4e Top 3-5 achievements: - Public access to Schomburg PDFs in S3 with improved error logging (53eaa33d...) - Automated Schomburg Collection creation via script (60a72359...) - Standardized ingestion across MET, HathiTrust, NYPL, LOC, DOAB, and MUSE via Unified RecordIngestor (multiple commits listed above) Impact and value: - Increased data availability and discoverability for Schomburg materials. - Reduced manual intervention in collection creation and ingestion, leading to faster time-to-publish and fewer operational errors. - Scalable, decoupled ingestion architecture enabling future cross-institution data flows and easier maintenance. Technologies/skills demonstrated: - AWS S3 ACLs and error handling improvements - Scripting for data collection and API integration - Design and adoption of a generic ingestion framework - Cross-system refactoring and modularization
8 Commits • 3 Features
Apr 1, 2025April 2025 — NYPL/drb-etl-pipeline: Key business outcomes and technical milestones. Summary: In April 2025, the drb-etl-pipeline delivered three core capabilities across Schomburg data access, collection orchestration, and ingestion scalability. 1) Public access to Schomburg PDFs in S3 was enabled by applying a public-read ACL on copy operations and by improving error logging for missing PDFs, reducing data access friction and improving reliability. 2) A Schomburg Collection creation script was added to automatically assemble collections by querying SCH Collection/Hathi files with cluster_status=true, collecting edition IDs, and POSTing to a local API with title/creator/description and edition IDs, enabling faster cataloging and richer metadata. 3) The Unified RecordIngestor framework was introduced to standardize ingestion across MET, HathiTrust, NYPL, LOC, DOAB, and MUSE, decoupling services and enabling a consistent, scalable ingestion flow. These efforts collectively improve data availability, quality, and operational efficiency, and lay groundwork for future expansion. Key commits and traceability: - Public PDFs in S3: 53eaa33d4317e94a2644426040dfe8d7d75bd906 - Schomburg Collection script: 60a72359cc6b61ed6348d26d14eaf2436dcfc97d - Unified RecordIngestor commits across MET/HathiTrust/NYPL/LOC/DOAB/MUSE: 59a9cb658d61edcbd25344e96cc219d71b4b63d6; 3f21aef8eb83b8cf2cb74f79ffac17693974032a; 0512d718950bb38ecb14db8b0bc01f941dae5d1a; 371c8d97a189fbbdc3ec2b59b2af53ac79d1134e; 26af1e1fd6920cc68c4a97836fd53fd6c27ab476; 0356de65a171e1431a6daac80205b3873feeed4e Top 3-5 achievements: - Public access to Schomburg PDFs in S3 with improved error logging (53eaa33d...) - Automated Schomburg Collection creation via script (60a72359...) - Standardized ingestion across MET, HathiTrust, NYPL, LOC, DOAB, and MUSE via Unified RecordIngestor (multiple commits listed above) Impact and value: - Increased data availability and discoverability for Schomburg materials. - Reduced manual intervention in collection creation and ingestion, leading to faster time-to-publish and fewer operational errors. - Scalable, decoupled ingestion architecture enabling future cross-institution data flows and easier maintenance. Technologies/skills demonstrated: - AWS S3 ACLs and error handling improvements - Scripting for data collection and API integration - Design and adoption of a generic ingestion framework - Cross-system refactoring and modularization
April 2025
March 2025
3 Commits • 3 Features
Mar 1, 2025March 2025 was focused on delivering reliability and developer enablement for NYPL/drb-etl-pipeline through a centralized PDF manifest workflow, targeted codebase improvements, and enhanced documentation. The work strengthens manifest lifecycle consistency, code readability, and onboarding for functional testing and CLI usage.
March 2025
3 Commits • 3 Features
Mar 1, 2025March 2025 was focused on delivering reliability and developer enablement for NYPL/drb-etl-pipeline through a centralized PDF manifest workflow, targeted codebase improvements, and enhanced documentation. The work strengthens manifest lifecycle consistency, code readability, and onboarding for functional testing and CLI usage.
February 2025
5 Commits • 2 Features
Feb 1, 2025February 2025 — NYPL/drb-etl-pipeline: CLACSO-focused enhancements to data ingestion, mapping, and test coverage. Delivered CLACSO Data Ingestion and Mapping Enhancements with a refactor of CLACSOMapping, integrated with the DSpace service, and added buffering plus enriched metadata extraction (authors, source IDs, media types, PDFs) to streamline ingestion and improve data quality. Introduced CLACSO Mapping Functional Tests to validate parsing and mapping of CLACSO records, ensuring reliable ingestion. Reduced potential data quality issues and laid groundwork for scalable CLACSO support in downstream workflows.
5 Commits • 2 Features
Feb 1, 2025February 2025 — NYPL/drb-etl-pipeline: CLACSO-focused enhancements to data ingestion, mapping, and test coverage. Delivered CLACSO Data Ingestion and Mapping Enhancements with a refactor of CLACSOMapping, integrated with the DSpace service, and added buffering plus enriched metadata extraction (authors, source IDs, media types, PDFs) to streamline ingestion and improve data quality. Introduced CLACSO Mapping Functional Tests to validate parsing and mapping of CLACSO records, ensuring reliable ingestion. Reduced potential data quality issues and laid groundwork for scalable CLACSO support in downstream workflows.
February 2025
January 2025
4 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for NYPL/drb-etl-pipeline: Delivered end-to-end analytics enhancements and CLACSO data integration, improving data coverage, accuracy, and metadata capabilities. Business impact includes more reliable usage reporting across all content and richer CLACSO records to support discovery and analytics. No major bugs reported this period.
January 2025
4 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for NYPL/drb-etl-pipeline: Delivered end-to-end analytics enhancements and CLACSO data integration, improving data coverage, accuracy, and metadata capabilities. Business impact includes more reliable usage reporting across all content and richer CLACSO records to support discovery and analytics. No major bugs reported this period.
December 2024
8 Commits • 3 Features
Dec 1, 2024December 2024 — NYPL/drb-etl-pipeline: Delivered end-to-end Publisher Backlist enhancements, consolidated ingestion, and cleanup capabilities, strengthening catalog accuracy and ingestion reliability while reducing operational risk. Key features delivered: - Publisher Backlist Manifest and Processing Enhancements: adds support for comma-separated ISBNs, generates and stores PDF manifests in S3, and creates webpub manifests for copyrighted and public domain items. Commits: 731f070487a68194759ce2c6536d12006120fbb5; fa1edf6dfc68179ae9aeeef6abe9e871cb685f55 - Consolidated Publisher Backlist Data Ingestion: removes outdated data ingestion sources (UofM and UofSC) and improves ingestion filtering to retrieve Airtable records marked as Ready to ingest. Commits: 92a68048e07d4dc1facbe41b396e7be4e90bf5cf; de635a76ce025282a67b3eab5b90db9ae1a03a5b; 5f245589ff3bd3c0abd45c9002289a2e3b43d046 - Backlist Deletion and Cleanup: adds deletion capabilities for publisher backlist (manifests, associated works and editions) from DB/Elasticsearch, and supports granular deletion of single editions. Commits: 300ab1b99d57225ea1906bc353c33f6e96bf46db; 4bd5ca7912abc464839e18afd10706ea5e20c7c4; ed8506f59cba13492cab0b823a919bd23348cad9 Major bugs fixed: - Implemented deletion/cleanup workflows to remove manifests, works, and editions, eliminating orphaned data and aligning DB/Elasticsearch state with publisher backlist lifecycle. Overall impact and accomplishments: - Improved data quality and consistency across the backlist, reducing manual intervention and enabling faster, reliable ingestion and deletion workflows. - Reduced dependency footprint by removing legacy ingestion sources, enabling a cleaner, more maintainable ETL pipeline. - Enhanced scalability and operational efficiency through S3-based manifest storage and automated webpub manifest generation. Technologies/skills demonstrated: - ETL pipeline design and maintenance, data ingestion lifecycle management, and deletions workflows - Cloud storage integration (S3) for manifest artifacts - Data modeling and cleanup in DB/Elasticsearch, with Airtable-based filtering - Manifest generation for webpubs and handling of comma-separated ISBNs - Version-controlled development with structured commit discipline
8 Commits • 3 Features
Dec 1, 2024December 2024 — NYPL/drb-etl-pipeline: Delivered end-to-end Publisher Backlist enhancements, consolidated ingestion, and cleanup capabilities, strengthening catalog accuracy and ingestion reliability while reducing operational risk. Key features delivered: - Publisher Backlist Manifest and Processing Enhancements: adds support for comma-separated ISBNs, generates and stores PDF manifests in S3, and creates webpub manifests for copyrighted and public domain items. Commits: 731f070487a68194759ce2c6536d12006120fbb5; fa1edf6dfc68179ae9aeeef6abe9e871cb685f55 - Consolidated Publisher Backlist Data Ingestion: removes outdated data ingestion sources (UofM and UofSC) and improves ingestion filtering to retrieve Airtable records marked as Ready to ingest. Commits: 92a68048e07d4dc1facbe41b396e7be4e90bf5cf; de635a76ce025282a67b3eab5b90db9ae1a03a5b; 5f245589ff3bd3c0abd45c9002289a2e3b43d046 - Backlist Deletion and Cleanup: adds deletion capabilities for publisher backlist (manifests, associated works and editions) from DB/Elasticsearch, and supports granular deletion of single editions. Commits: 300ab1b99d57225ea1906bc353c33f6e96bf46db; 4bd5ca7912abc464839e18afd10706ea5e20c7c4; ed8506f59cba13492cab0b823a919bd23348cad9 Major bugs fixed: - Implemented deletion/cleanup workflows to remove manifests, works, and editions, eliminating orphaned data and aligning DB/Elasticsearch state with publisher backlist lifecycle. Overall impact and accomplishments: - Improved data quality and consistency across the backlist, reducing manual intervention and enabling faster, reliable ingestion and deletion workflows. - Reduced dependency footprint by removing legacy ingestion sources, enabling a cleaner, more maintainable ETL pipeline. - Enhanced scalability and operational efficiency through S3-based manifest storage and automated webpub manifest generation. Technologies/skills demonstrated: - ETL pipeline design and maintenance, data ingestion lifecycle management, and deletions workflows - Cloud storage integration (S3) for manifest artifacts - Data modeling and cleanup in DB/Elasticsearch, with Airtable-based filtering - Manifest generation for webpubs and handling of comma-separated ISBNs - Version-controlled development with structured commit discipline
December 2024
November 2024
3 Commits • 3 Features
Nov 1, 2024Month: 2024-11 Key features delivered: - Test Suite Cleanup and Readability Improvements: Refactored test_fulfill_manifest_process.py to snake_case, renamed variables and mocks, and removed unused code to improve readability and maintainability without changing tested functionality. - Publisher Backlist Import via Airtable API: Added a new PublisherBacklistProcess and Airtable integration utility to fetch publisher backlists and collections from Airtable using API key authentication. - Publisher Backlist Data Mapping: Introduced a new data mapping for publisher backlist records, updated services to use the new mapping, and added unit tests to ensure proper ingestion and processing. Major bugs fixed: - No explicit major bugs fixed this month; stability improvements were achieved through test cleanup and the introduction of a robust backlist ingestion path with data mapping. Overall impact and accomplishments: - Improved test readability and maintainability, reducing future maintenance costs and speeding up local and CI validation. - Enabled reliable ingestion of publisher backlists from Airtable, expanding data sources and enabling timely updates to downstream consumers. - Implemented a structured data mapping for publisher backlists with accompanying unit tests, improving data quality and processing reliability. - Built foundational capabilities for production-grade Airtable-backed integrations within the ETL pipeline. Technologies/skills demonstrated: - Python, pytest-based testing, and test refactoring practices (snake_case, mocks, variable naming). - Integration with Airtable API, including API-key authentication flow. - Data modeling and mapping, service-layer updates, and unit testing for ingestion pathways. - Emphasis on maintainability, code readability, and reliability of ETL components.
November 2024
3 Commits • 3 Features
Nov 1, 2024Month: 2024-11 Key features delivered: - Test Suite Cleanup and Readability Improvements: Refactored test_fulfill_manifest_process.py to snake_case, renamed variables and mocks, and removed unused code to improve readability and maintainability without changing tested functionality. - Publisher Backlist Import via Airtable API: Added a new PublisherBacklistProcess and Airtable integration utility to fetch publisher backlists and collections from Airtable using API key authentication. - Publisher Backlist Data Mapping: Introduced a new data mapping for publisher backlist records, updated services to use the new mapping, and added unit tests to ensure proper ingestion and processing. Major bugs fixed: - No explicit major bugs fixed this month; stability improvements were achieved through test cleanup and the introduction of a robust backlist ingestion path with data mapping. Overall impact and accomplishments: - Improved test readability and maintainability, reducing future maintenance costs and speeding up local and CI validation. - Enabled reliable ingestion of publisher backlists from Airtable, expanding data sources and enabling timely updates to downstream consumers. - Implemented a structured data mapping for publisher backlists with accompanying unit tests, improving data quality and processing reliability. - Built foundational capabilities for production-grade Airtable-backed integrations within the ETL pipeline. Technologies/skills demonstrated: - Python, pytest-based testing, and test refactoring practices (snake_case, mocks, variable naming). - Integration with Airtable API, including API-key authentication flow. - Data modeling and mapping, service-layer updates, and unit testing for ingestion pathways. - Emphasis on maintainability, code readability, and reliability of ETL components.
October 2024
2 Commits • 2 Features
Oct 1, 2024October 2024 monthly summary for NYPL/drb-etl-pipeline focused on delivering maintainable, reliable data ingestion features and improving data freshness. Implemented refactoring and manifest/storage enhancements for Chicago ISAC ingestion, and optimized HathiTrust daily ingest with a 24-hour window. These changes reduce operational risk, improve data quality, and lay groundwork for future enhancements.
2 Commits • 2 Features
Oct 1, 2024October 2024 monthly summary for NYPL/drb-etl-pipeline focused on delivering maintainable, reliable data ingestion features and improving data freshness. Implemented refactoring and manifest/storage enhancements for Chicago ISAC ingestion, and optimized HathiTrust daily ingest with a 24-hour window. These changes reduce operational risk, improve data quality, and lay groundwork for future enhancements.
October 2024
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Quality Metrics
Correctness84.2%
Maintainability83.4%
Architecture83.0%
Performance73.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
MakefileMarkdownPythonSQL
Technical Skills
API IntegrationAWS S3AnalyticsBackend DevelopmentCloud ComputingCloud ServicesCloud Services (AWS S3)Cloud Services (S3)Cloud Storage (S3)Cloud Storage ManagementCode RefactoringCode StyleData EngineeringData IngestionData Mapping
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