LMS & EdTech Data Pipelines
Gradebook · Attendance · Engagement — engineered for production scale and FERPA compliance.
This site is a working reference for the engineering and data teams that keep institutional learning platforms in sync. Whether you are reconciling a Canvas gradebook at semester close, normalizing attendance states across Moodle and Blackboard, or staring down a 429 from a queue worker at 3am — the patterns here are the ones that survive contact with real student data at real scale.
Every guide is written for practitioners: Python automation builders, EdTech engineers, institutional data analysts, and academic IT teams. The focus is on decoupled architectures, deterministic transformation logic, idempotent sync jobs, observable error recovery, and the compliance boundaries (FERPA, PII, audit logging) that turn a “working script” into a production-grade pipeline.
Pick a section below to dive in — or follow the breadcrumb trails from any topic to its deeper sub-pages.
Explore the three pillars
Each section starts with an architectural overview, then drills down into focused topics with code-level guidance.
LMS Data Architecture & Schema Mapping
Vendor-specific schema models for Canvas, Moodle, and Blackboard, identity resolution, CSV export standards, and the architectural patterns that hold them together.
Read the guideAPI Ingestion & Sync Workflows
Resilient extraction patterns: Python Requests, async polling, pagination at scale, retry logic with jitter, and surviving Canvas API rate limits.
Read the guideGradebook & Attendance Normalization
Canonical models for weighted grades and attendance states across heterogeneous LMS exports, with deterministic transformation rules.
Read the guide