Data Pipeline System

Data infrastructure component of education platform (professional work): **Project Context**: - Needed to integrate data from multiple sources for analytics - Part of infrastructure at ZhiHui BianJie **Technical Work**: - Built ETL pipelines with Apache Airflow - Processed daily records from MySQL, REST APIs, CSV files - Implemented data validation and quality checks - Optimized query performance when pipeline slowed down - Set up monitoring and alerting for failures - Designed data warehouse schema for analytics **Challenges Encountered**: - Database became bottleneck as data grew - Learned about connection pooling and batch processing - Debugging pipeline failures (data format changes, API timeouts) - Balancing freshness vs. resource usage **What I Learned**: - Data engineering is harder than it looks - Importance of data quality checks - Pipeline orchestration and dependency management - SQL optimization through trial and error **Reality**: Execution time improved significantly, but required multiple iterations to get right. Cannot share specific throughput numbers (company confidential).