edanalytics/earthmover_edfi_bundles
Oct 2025 – Oct 2025
1 Month active
Languages Used
CSVJSONPythonYAMLcsvjsonyaml
Technical Skills
Data EngineeringData ModelingData TransformationData ValidationETLEd-Fi Implementation
Bruk
PROFILE
Bruk
Bruk developed and delivered Ed-Fi assessment data ingestion pipelines for KRA and myIGDIs within the edanalytics/earthmover_edfi_bundles repository. Using Python and YAML, Bruk designed end-to-end ETL workflows that map assessment data to the Ed-Fi data model, incorporating configuration files, templates, and seed data to ensure accurate mapping of student IDs and performance levels. The work included a pre-processing step for myIGDIs, improving data formatting and processing speed. Comprehensive documentation and sample data were provided to support ingestion and validation in staging and production, reducing manual data wrangling and enabling more reliable, automated analytics for education partners.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
1,435
Activity Months1
Your Network
7 people
Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 — Implemented and delivered Ed-Fi assessment data ingestion pipelines for KRA and myIGDIs within edanalytics/earthmover_edfi_bundles. The work provides end-to-end ingestion and mapping to the Ed-Fi data model, enabling automated validation and downstream analytics. The KRA pipeline includes configuration files, templates, and seed data to correctly map student IDs, performance levels, and assessments. MyIGDIs received a pre-processing step for data formatting, resulting in improved data quality and processing performance. Comprehensive documentation and configuration for Earthmover and Lightbeam, plus sample data, support easy ingestion and validation in staging/production. These changes reduce manual data wrangling, accelerate reporting cycles, and improve trust in analytics for education partners.
2 Commits • 1 Features
Oct 1, 2025October 2025 — Implemented and delivered Ed-Fi assessment data ingestion pipelines for KRA and myIGDIs within edanalytics/earthmover_edfi_bundles. The work provides end-to-end ingestion and mapping to the Ed-Fi data model, enabling automated validation and downstream analytics. The KRA pipeline includes configuration files, templates, and seed data to correctly map student IDs, performance levels, and assessments. MyIGDIs received a pre-processing step for data formatting, resulting in improved data quality and processing performance. Comprehensive documentation and configuration for Earthmover and Lightbeam, plus sample data, support easy ingestion and validation in staging/production. These changes reduce manual data wrangling, accelerate reporting cycles, and improve trust in analytics for education partners.
October 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVJSONPythonYAMLcsvjsonyaml
Technical Skills
Data EngineeringData ModelingData TransformationData ValidationETLEd-Fi ImplementationEd-Fi IntegrationScripting
Repositories Contributed To
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