NEONScience/NEON-IS-data-processing
Nov 2024 – Oct 2025
10 Months active
Languages Used
BashPythonRYAMLShellDockerfileRscriptpython
Technical Skills
Cloud InfrastructureData EngineeringData ProcessingDevOpsPipeline OrchestrationQuality Control
burlingamet
PROFILE
Burlingamet
Over ten months, Tom Burlingame engineered and maintained robust data processing pipelines for the NEONScience/NEON-IS-data-processing repository, focusing on scalable sensor data ingestion, calibration, and quality control. He implemented modular, configuration-driven workflows using Python, R, and Docker, enabling reproducible CI/CD, real-time Kafka-based ingestion, and automated data quality flagging. His work included algorithm optimization, schema management, and integration with cloud storage, supporting both daily and real-time analytics. By refactoring legacy code, enhancing error handling, and modernizing deployment with GitHub Actions, Tom improved data reliability, processing throughput, and deployment velocity, delivering a maintainable foundation for ongoing scientific data analysis and reporting.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
207Total
Bugs
32
Commits
207
Features
65
Lines of code
21,068
Activity Months10
Your Network
19 peopleSame Organization
@battelleecology.org8
Work History
October 2025
20 Commits • 4 Features
Oct 1, 2025Monthly summary for 2025-10: Delivered significant CI/CD hardening, pipeline reliability improvements, and data-processing robustness for NEON-IS-data-processing. Key features delivered include CI/CD improvements for development and CERT environments (consolidated GitHub Actions workflows, added development pipelines, aligned workflow names, and enabled master and CERT pipelines), and Docker image tag upgrades for RadShortPrimary and QAQC pipelines to incorporate the latest builds. ShadowFlag testing scope expanded with longer test windows and updated cron scheduling, and ongoing code quality/refactoring reduced legacy threshold logic. Major bug fixes included robustness enhancements in R data processing (improved error handling and fail-fast behavior to preserve data integrity, including adjustments to stop/flag logic). These efforts improved deployment velocity, data integrity, and test visibility, delivering clearer feedback loops and reduced toil. Technologies/skills demonstrated include GitHub Actions CI/CD, Docker image tagging, R data processing error handling, test scheduling, and code refactoring.
20 Commits • 4 Features
Oct 1, 2025Monthly summary for 2025-10: Delivered significant CI/CD hardening, pipeline reliability improvements, and data-processing robustness for NEON-IS-data-processing. Key features delivered include CI/CD improvements for development and CERT environments (consolidated GitHub Actions workflows, added development pipelines, aligned workflow names, and enabled master and CERT pipelines), and Docker image tag upgrades for RadShortPrimary and QAQC pipelines to incorporate the latest builds. ShadowFlag testing scope expanded with longer test windows and updated cron scheduling, and ongoing code quality/refactoring reduced legacy threshold logic. Major bug fixes included robustness enhancements in R data processing (improved error handling and fail-fast behavior to preserve data integrity, including adjustments to stop/flag logic). These efforts improved deployment velocity, data integrity, and test visibility, delivering clearer feedback loops and reduced toil. Technologies/skills demonstrated include GitHub Actions CI/CD, Docker image tagging, R data processing error handling, test scheduling, and code refactoring.
October 2025
September 2025
27 Commits • 8 Features
Sep 1, 2025September 2025 monthly summary for NEON-IS-data-processing (NEONScience/NEON-IS-data-processing). Delivered substantial data-processing improvements and foundational CI/CD and testing infrastructure. Highlights include: continued CMP pipeline evolution with new schema support for standard flags and a second schema for custom QFs; stability fixes across calibration data handling and data_source_gcs setup; improved robustness for parsing wrapper calls; enhanced threshold/NA/azimuth handling for reliable flagging; and extensive CI/CD scaffolding with a Dockerfile and GitHub Actions, plus schema integration and version tagging. Also introduced generalized scripting to support multiple sources and sensor types, and established portal publication testing pipelines with pipe_list.txt generation. These changes improve data quality, enable scalable future features, and accelerate production deployments.
September 2025
27 Commits • 8 Features
Sep 1, 2025September 2025 monthly summary for NEON-IS-data-processing (NEONScience/NEON-IS-data-processing). Delivered substantial data-processing improvements and foundational CI/CD and testing infrastructure. Highlights include: continued CMP pipeline evolution with new schema support for standard flags and a second schema for custom QFs; stability fixes across calibration data handling and data_source_gcs setup; improved robustness for parsing wrapper calls; enhanced threshold/NA/azimuth handling for reliable flagging; and extensive CI/CD scaffolding with a Dockerfile and GitHub Actions, plus schema integration and version tagging. Also introduced generalized scripting to support multiple sources and sensor types, and established portal publication testing pipelines with pipe_list.txt generation. These changes improve data quality, enable scalable future features, and accelerate production deployments.
August 2025
14 Commits • 4 Features
Aug 1, 2025Concise August 2025 monthly summary for NEON-IS-data-processing (NEONScience/NEON-IS-data-processing). Focused on delivering robust data processing pipelines, improving data quality, and stabilizing processing for scalable daily ingestion. Highlights include modernization of precipitation data processing with DAG-based workflows and GCS integration; L1 RadShortPrimary pipeline setup and fixes; data integrity and reproducibility hardening; data quality maintenance; and testing/refinement of precipWeighing pipeline environments. Overall, these efforts improved reliability, throughput, and business value for downstream analyses.
14 Commits • 4 Features
Aug 1, 2025Concise August 2025 monthly summary for NEON-IS-data-processing (NEONScience/NEON-IS-data-processing). Focused on delivering robust data processing pipelines, improving data quality, and stabilizing processing for scalable daily ingestion. Highlights include modernization of precipitation data processing with DAG-based workflows and GCS integration; L1 RadShortPrimary pipeline setup and fixes; data integrity and reproducibility hardening; data quality maintenance; and testing/refinement of precipWeighing pipeline environments. Overall, these efforts improved reliability, throughput, and business value for downstream analyses.
August 2025
July 2025
36 Commits • 8 Features
Jul 1, 2025July 2025 monthly summary for NEON-IS-data-processing: Delivered data collection improvements, substantial algorithm optimizations, enhanced data ingestion, and deployment/process improvements with a focus on reliability and business value.
July 2025
36 Commits • 8 Features
Jul 1, 2025July 2025 monthly summary for NEON-IS-data-processing: Delivered data collection improvements, substantial algorithm optimizations, enhanced data ingestion, and deployment/process improvements with a focus on reliability and business value.
June 2025
34 Commits • 13 Features
Jun 1, 2025June 2025 (NEON-IS-data-processing) monthly summary focused on key features, stability, and deployment readiness. The NEON-IS data-processing module delivered several calibrated data enhancements, architecture improvements, and improved CI/CD and testing readiness, enabling faster iteration, more reliable processing, and better traceability across multi-site workflows. Key features delivered: - Calibrations: A0→A1 conversion and tipping bucket calibration function — enabling more accurate calibration pipelines for tipping bucket sensors and consistent downstream reporting. Notable commits include updated calibration function (211d46927de4c3891195747fa38379374477edd3) and the new tipping bucket calibration function (ea42554053b6902f5e8504fa9893c4aa686b03b5). - Grouped processing refactor and code cleanup — improved processing throughput and maintainability by switching to grouped processing, with cleanup/rename and progress tracking for custom code. Commits include changing to grouped processing (7cde5a4bf521d5d3ae46670b6f71c2542a06adfa), cleanup/rename (9a9f7e58f444bcdcf2ba8b2b2aab82644e45ce7a), and custom code progress (488c54e50b919b1bccbbaa491e72043ed904f4f6). - CI/CD readiness and containerization improvements — prepared dockerfiles and GitHub Actions scripts for master deployment, along with workflow updates to support the precip module. Notable commits include dockerfiles and actions readiness (86529dc2a12f2e8c0fd0bff9ab1d2f044f2c83d0), workflow updates (d58a2709e1310bda150038d11c2288517c82ef5c), and related specs refinements (af81e2e24e62a036f2901db8b3775cea45f7f78e). - Site lists and testing readiness — updated site lists for hornetQ testing and included aqua site for broader validation. Commits bf5eb24ff39f01762b163773bce01a902f191547 and e850de00e5f3f5a9d7e2e6a350126d4fcf5511cf. - Stability, debugging, and observability enhancements — added enhanced debugging logs, troubleshooting diagnostics, and fixes addressing various stability gaps (Kafka blocking, Info vs info casing, function call) and observer-friendly logs. Key commits include temporary Kafka block (ef0a9011de3486094dfc52f7eb2959b3716231fd), Info casing alignment (f9e7081ce996122418d83008ce9b858f028d61b8), and function call fix (fd82ad90ad61825c76a7a11ff8b934350e7f691e), plus added debug logs (86ddc259c8bfe03010245e5d833eae14ef3d70e0, 7328b377d6072fa16525afb12c73183ecd34e16d). Overall impact and accomplishments: - Increased calibration accuracy and data quality, reducing downstream data gaps and improving trust in sensor-derived metrics. - Improved processing performance and maintainability via grouped processing and code cleanup, enabling faster onboarding of new sensors and easier future refactors. - Production-readiness advanced with Docker+GitHub Actions setup and updated CI/CD workflows, reducing deployment risk and enabling reproducible builds. - Enhanced testing, observability, and traceability across multi-site configurations with updated site lists, SHA tagging alignment, and improved debugging capabilities. Technologies/skills demonstrated: - Data processing pipelines (Python), data calibration and flow controls, and multi-site data handling. - Containerization and CI/CD (Docker, GitHub Actions) for streamlined deployment. - Configuration and schema management, including schema updates and logging/diagnostics enhancements. - Troubleshooting, debugging instrumentation, and performance-oriented refactors.
34 Commits • 13 Features
Jun 1, 2025June 2025 (NEON-IS-data-processing) monthly summary focused on key features, stability, and deployment readiness. The NEON-IS data-processing module delivered several calibrated data enhancements, architecture improvements, and improved CI/CD and testing readiness, enabling faster iteration, more reliable processing, and better traceability across multi-site workflows. Key features delivered: - Calibrations: A0→A1 conversion and tipping bucket calibration function — enabling more accurate calibration pipelines for tipping bucket sensors and consistent downstream reporting. Notable commits include updated calibration function (211d46927de4c3891195747fa38379374477edd3) and the new tipping bucket calibration function (ea42554053b6902f5e8504fa9893c4aa686b03b5). - Grouped processing refactor and code cleanup — improved processing throughput and maintainability by switching to grouped processing, with cleanup/rename and progress tracking for custom code. Commits include changing to grouped processing (7cde5a4bf521d5d3ae46670b6f71c2542a06adfa), cleanup/rename (9a9f7e58f444bcdcf2ba8b2b2aab82644e45ce7a), and custom code progress (488c54e50b919b1bccbbaa491e72043ed904f4f6). - CI/CD readiness and containerization improvements — prepared dockerfiles and GitHub Actions scripts for master deployment, along with workflow updates to support the precip module. Notable commits include dockerfiles and actions readiness (86529dc2a12f2e8c0fd0bff9ab1d2f044f2c83d0), workflow updates (d58a2709e1310bda150038d11c2288517c82ef5c), and related specs refinements (af81e2e24e62a036f2901db8b3775cea45f7f78e). - Site lists and testing readiness — updated site lists for hornetQ testing and included aqua site for broader validation. Commits bf5eb24ff39f01762b163773bce01a902f191547 and e850de00e5f3f5a9d7e2e6a350126d4fcf5511cf. - Stability, debugging, and observability enhancements — added enhanced debugging logs, troubleshooting diagnostics, and fixes addressing various stability gaps (Kafka blocking, Info vs info casing, function call) and observer-friendly logs. Key commits include temporary Kafka block (ef0a9011de3486094dfc52f7eb2959b3716231fd), Info casing alignment (f9e7081ce996122418d83008ce9b858f028d61b8), and function call fix (fd82ad90ad61825c76a7a11ff8b934350e7f691e), plus added debug logs (86ddc259c8bfe03010245e5d833eae14ef3d70e0, 7328b377d6072fa16525afb12c73183ecd34e16d). Overall impact and accomplishments: - Increased calibration accuracy and data quality, reducing downstream data gaps and improving trust in sensor-derived metrics. - Improved processing performance and maintainability via grouped processing and code cleanup, enabling faster onboarding of new sensors and easier future refactors. - Production-readiness advanced with Docker+GitHub Actions setup and updated CI/CD workflows, reducing deployment risk and enabling reproducible builds. - Enhanced testing, observability, and traceability across multi-site configurations with updated site lists, SHA tagging alignment, and improved debugging capabilities. Technologies/skills demonstrated: - Data processing pipelines (Python), data calibration and flow controls, and multi-site data handling. - Containerization and CI/CD (Docker, GitHub Actions) for streamlined deployment. - Configuration and schema management, including schema updates and logging/diagnostics enhancements. - Troubleshooting, debugging instrumentation, and performance-oriented refactors.
June 2025
May 2025
8 Commits • 6 Features
May 1, 2025May 2025 performance summary for NEON-IS-data-processing: Delivered a suite of Kafka-driven, real-time ingestion and data pipeline enhancements, modernizing sensor data processing and precipitation data workflows. Implemented core infrastructure updates to support timelier, more reliable data delivery and easier schema evolution, aligning with business needs for faster insights and higher data quality.
May 2025
8 Commits • 6 Features
May 1, 2025May 2025 performance summary for NEON-IS-data-processing: Delivered a suite of Kafka-driven, real-time ingestion and data pipeline enhancements, modernizing sensor data processing and precipitation data workflows. Implemented core infrastructure updates to support timelier, more reliable data delivery and easier schema evolution, aligning with business needs for faster insights and higher data quality.
April 2025
49 Commits • 17 Features
Apr 1, 2025April 2025: Delivered a substantial overhaul of the NEON-IS-data-processing pipeline, focusing on precise time alignment, modular parsing, and end-to-end integration to unlock faster 1-minute data analytics and richer flagging. The work enabled seamless GCS data source ingestion, robust time-shift operations (forward/backward with padding), and coordinated QA/QC flags across modules. These changes reduced data-lag, improved data quality, and laid groundwork for upcoming Level 1 analytics.
49 Commits • 17 Features
Apr 1, 2025April 2025: Delivered a substantial overhaul of the NEON-IS-data-processing pipeline, focusing on precise time alignment, modular parsing, and end-to-end integration to unlock faster 1-minute data analytics and richer flagging. The work enabled seamless GCS data source ingestion, robust time-shift operations (forward/backward with padding), and coordinated QA/QC flags across modules. These changes reduced data-lag, improved data quality, and laid groundwork for upcoming Level 1 analytics.
April 2025
March 2025
14 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for NEON-IS data processing (NEONScience/NEON-IS-data-processing). Delivered end-to-end Pluvio data pipeline enhancements with location-aware processing and calibration integration, expanded precipitation weighing pipelines with robust quality metrics, and completed essential maintenance fixes to improve data integrity and deployment stability. The work improves data reliability for downstream analyses, strengthens data lineage, and supports scalable pipeline architecture across the repo.
March 2025
14 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for NEON-IS data processing (NEONScience/NEON-IS-data-processing). Delivered end-to-end Pluvio data pipeline enhancements with location-aware processing and calibration integration, expanded precipitation weighing pipelines with robust quality metrics, and completed essential maintenance fixes to improve data integrity and deployment stability. The work improves data reliability for downstream analyses, strengthens data lineage, and supports scalable pipeline architecture across the repo.
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for NEONScience/NEON-IS-data-processing focused on reproducible CI/testing, expanded data processing capabilities for Pluvio, and aligning testing windows to improve reliability. These efforts reduced CI flakiness, enhanced ingestion/processing workflows, and tightened validation timelines for early 2025.
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for NEONScience/NEON-IS-data-processing focused on reproducible CI/testing, expanded data processing capabilities for Pluvio, and aligning testing windows to improve reliability. These efforts reduced CI flakiness, enhanced ingestion/processing workflows, and tightened validation timelines for early 2025.
February 2025
November 2024
2 Commits • 1 Features
Nov 1, 2024Concise monthly summary for 2024-11 focusing on business value and technical achievements in NEONScience/NEON-IS-data-processing. The month delivered enhancements to precipitation data processing with QC updates and configurable pipelines, improving data quality, reliability, and downstream analytics readiness.
November 2024
2 Commits • 1 Features
Nov 1, 2024Concise monthly summary for 2024-11 focusing on business value and technical achievements in NEONScience/NEON-IS-data-processing. The month delivered enhancements to precipitation data processing with QC updates and configurable pipelines, improving data quality, reliability, and downstream analytics readiness.
Activity
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Quality Metrics
Correctness85.0%
Maintainability85.8%
Architecture82.2%
Performance75.0%
AI Usage21.0%
Skills & Technologies
Programming Languages
BashDockerfileJSONPythonRRscriptShellYAMLbashpython
Technical Skills
AI-assisted DevelopmentAlgorithm RefinementAutomated WorkflowsBash ScriptingCI/CDCalibrationCloud ComputingCloud InfrastructureCloud StorageCloud Storage IntegrationCode CleanupCode RefactoringCode cleanupConfiguration ManagementContainerization
Repositories Contributed To
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