Start by clarifying requirements and scale (e.g., number of schools, users, messages per day, data retention, privacy constraints like FERPA/COPPA). Then design a batch-oriented ETL pipeline that ingests from Slack-like APIs and databases, transforms data into a star schema optimized for analytics (e.g., engagement, safety, and usage metrics), and loads it into a data warehouse. Discuss trade-offs between batch vs. streaming, data modeling choices, and how to handle sensitive student data.
Pro tip: Emphasize data governance and privacy from the start—school data involves minors, so compliance (FERPA, COPPA, GDPR) is non-negotiable. Also, propose a hybrid approach: batch for cost-efficiency on historical data and micro-batch/streaming for near-real-time safety alerts.
Ask about data sources (message logs, user events, channel metadata), volume (e.g., 1M messages/day), latency needs (daily reports vs. real-time alerts), and compliance requirements (FERPA, COPPA). Define success metrics like daily active users, message engagement, and safety incident detection.
Propose a star schema with fact tables (messages, user_activity) and dimension tables (users, channels, schools, time). Include slowly changing dimensions for user roles and school affiliations. Discuss partitioning by date and school_id for query performance.
Outline stages: extraction via APIs/CDC from source databases, transformation using Spark or SQL for cleansing, enrichment (e.g., sentiment analysis, PII masking), and loading into a warehouse (e.g., Snowflake, BigQuery). Consider orchestration with Airflow and data quality checks.
Compare batch vs. streaming (e.g., Kafka + Flink for real-time safety alerts). Discuss cost vs. latency, and how to handle schema evolution and late-arriving data. Propose a lambda architecture if both batch and real-time are needed.
Detail encryption at rest and in transit, role-based access control, and data anonymization for analytics. Mention audit logs and retention policies to comply with educational privacy laws.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.