Design a 25,000-CSV ETL pipeline
Design an automated ETL pipeline to preprocess 25,000 CSV files and load the results into a centralized database. The pipeline should handle inconsistent schemas, duplicate records, late arrivals, and occasional corruption. The target is a single analytics-ready table for downstream analysts and dashboards.
Your design should explain:
- File Ingestion: How files are received (e.g., manual upload, shared drive, SFTP, object storage, API).
- Triggering Mechanism: What initiates extraction and transformation (e.g., cron job, polling, event-driven notifications, message queue).
- Automation Levels: How manual, semi-automated, and fully automated workflows are distinguished.
- Data Quality & Robustness: Implementation details for schema validation, type standardization, deduplication, bad-row handling, retries, and idempotency.
- Database Location: Where the centralized database resides (e.g., local server, on-premise, cloud warehouse).
- Productionization: Evidence of a truly productionized pipeline.
AI interview prep
Core dilemma
Balancing the need for a robust, automated, and scalable data ingestion and transformation process against the inherent messiness and variability of real-world data sources (25,000 CSVs with potential inconsistencies and errors).
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a 25,000-CSV ETL pipeline
Design an automated ETL pipeline to preprocess 25,000 CSV files and load the results into a centralized database. The pipeline should handle inconsistent schemas, duplicate records, late arrivals, and occasional corruption. The target is a single analytics-ready table for downstream analysts and dashboards.
Your design should explain:
- File Ingestion: How files are received (e.g., manual upload, shared drive, SFTP, object storage, API).
- Triggering Mechanism: What initiates extraction and transformation (e.g., cron job, polling, event-driven notifications, message queue).
- Automation Levels: How manual, semi-automated, and fully automated workflows are distinguished.
- Data Quality & Robustness: Implementation details for schema validation, type standardization, deduplication, bad-row handling, retries, and idempotency.
- Database Location: Where the centralized database resides (e.g., local server, on-premise, cloud warehouse).
- Productionization: Evidence of a truly productionized pipeline.