All Case Studies / RADCLIFFE.HARVARD.EDU
Python Data Engineering 12 weeks 3 engineers

RADCLIFFE.HARVARD.EDU

Turning Terabytes of Academic Data Into Actionable Research Insights

2.3TB
Data Processed
94%
Time Saved
15
Data Sources
100%
IRB Compliant

The Challenge

Harvard Radcliffe Institute's fellowship programs generate vast amounts of research data across multiple disciplines — from gender studies to public policy to scientific research. Researchers were spending 60% of their time on data wrangling rather than analysis. The institute needed automated pipelines to extract data from 15+ heterogeneous sources (surveys, databases, APIs, document collections), transform it into analysis-ready formats, and generate standardized reports — all while maintaining strict IRB compliance for sensitive research data.

The Solution

We built a Python-based data engineering platform using Apache Airflow for workflow orchestration. Custom extraction adapters handle each data source type: REST APIs, SQL databases, Excel/CSV files, and even OCR for historical documents. The transformation layer uses pandas and dask for processing datasets that don't fit in memory, with automated data quality checks at each stage. A Jupyter-based analysis environment gives researchers interactive access to clean data, while automated report generation delivers weekly summaries in publication-ready formats. All data flows through encrypted channels with comprehensive audit logging for IRB compliance.

Data Pipeline

Automated ETL workflows

Analysis Tools

Statistical modeling & insights

Report Generation

Automated research reports

Data Security

IRB-compliant handling

Build Process

Phase 1 2 weeks

Discovery & Data Audit

Catalogued 15 data sources, mapped researcher workflows, identified data quality issues, and designed the pipeline architecture with IRB compliance requirements.

Phase 2 4 weeks

Pipeline Development

Built Apache Airflow orchestration layer, developed custom extractors for each data source, implemented transformation pipelines with dask for large datasets.

Phase 3 3 weeks

Analysis & Reporting

Created Jupyter notebook templates for common analyses, built automated report generation system, implemented visualization library for research outputs.

Phase 4 1 week

Security & Deployment

End-to-end encryption implementation, audit logging for IRB compliance, researcher training sessions, and production deployment with monitoring.

Total: 12 weeks from kickoff to production

Tech Stack

The technologies and services powering RADCLIFFE.HARVARD.EDU.

Python 3.11
Core
Apache Airflow
Orchestration
pandas/dask
Data Processing
PostgreSQL
Database
Jupyter
Analysis
Docker
Containers
AWS S3
Storage
Matplotlib
Visualization

Results & Impact

Automated extraction and transformation of 2.3TB of research data from 15 sources

Reduced researcher data preparation time by 94% — from 60% of work hours to under 5%

Full IRB compliance with encrypted data flows and comprehensive audit logging

Weekly automated reports reduced manual reporting effort by 20 hours per week

Jupyter analysis environment adopted by 100% of active research fellows

Pipeline architecture supports addition of new data sources without code changes

Want Something Like This?

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12 weeks
Timeline
3 engineers
Team
2016
Year
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