Case study
Data Pipeline Platform (Japan EdTech)
Built an ETL and analytics platform that turned fragmented academic data into automated insights for Japanese education workflows.
- Role
- Junior Software Architect
- Published
- Tags
- edtech · data-pipeline · etl · ai · analytics
Student records
10K+ / month
Monthly academic data processing volume
Insight generation
Automated
AI-assisted analysis and BI reporting
Problem
Academic data was fragmented, and teams lacked a reliable way to turn operational records into usable insights. Manual reporting slowed decision-making and made it hard to understand learning patterns across classrooms.
Solution
I designed and built an ETL platform using Kafka, Airflow, and PySpark. The platform standardized data ingestion, transformed academic records into analytics-ready datasets, integrated AI-assisted insight generation with OpenAI and LangChain, and exposed outcomes through BI dashboards.
Architecture decisions
- Kafka separated ingestion from processing so upstream data changes would not directly break analytics workflows.
- Airflow made scheduled processing, retries, and pipeline dependencies explicit.
- PySpark handled scalable transformation for academic records, while BI dashboards and AI-assisted summaries made the data usable for stakeholders.
Impact
- Processed 10K+ student records monthly.
- Automated insight generation for academic data analysis.
- Reduced friction between raw educational data and actionable business or classroom insights.