Airbnb-Specific Data Patterns Questions
Domain-specific data modeling and analytics patterns used in Airbnb-scale product analytics. Covers data schema design, event and transaction patterns, feature engineering templates for predictive models, cohort and lifecycle analytics, geospatial and temporal data patterns, price and demand forecasting signals, AB testing data patterns, and data quality, governance, and lineage considerations relevant to Airbnb data.
HardSystem Design
0 practiced
Propose a global data partitioning and replication strategy for analytical datasets across multiple cloud regions to provide low-latency reads in each region while minimizing replication and egress costs. Discuss nearline vs archival tiers, regional hot partitions, eventual consistency implications, and mechanisms for discovering the freshest regional copy.
MediumTechnical
0 practiced
Design an OLAP schema for a bookings fact table to support revenue attribution, promotions, taxes, and multi-currency reporting. Include suggested columns (raw amounts, breakdowns, converted_amount, currency_code, promo_id, fee_ids), data types, partitioning keys, and discuss when to normalize fees into separate dimension tables vs denormalizing into the fact.
EasyTechnical
0 practiced
What is a star schema and why is it commonly used for product analytics at Airbnb? Describe the fact table(s) and at least three dimension tables you would create for bookings analytics. For each table, give sample columns and typical join keys and explain how this model supports analytic queries.
HardTechnical
0 practiced
Propose a schema and ETL design to build a guest-host interaction graph for fraud detection that supports k-hop neighbor queries. Include choices between graph databases (e.g., Neo4j) vs adjacency tables in data warehouse, how to integrate streaming updates from bookings/messages, and approaches for scalable graph traversal or offline graph feature extraction for ML.
MediumTechnical
0 practiced
How would you implement deterministic sampling to produce a reproducible 0.1% sample of events across multiple pipelines for debugging? Describe hashing strategy, choice of key(s) to hash (user_id, event_id), how to avoid bias, and how to change sample rates while preserving history.
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