Lyft-Specific Data Modeling & Analytics Requirements Questions
Lyft-specific data modeling and analytics requirements for data platforms, including ride event data, trip-level schemas, driver and rider dimensions, pricing and surge data, geospatial/location data, and analytics needs such as reporting, dashboards, and real-time analytics. Covers analytic schema design (star/snowflake), ETL/ELT patterns, data quality and governance at scale, data lineage, privacy considerations, and integration with the broader data stack (data lake/warehouse, streaming pipelines).
HardTechnical
0 practiced
Propose a cloud cost-optimization plan for Lyft's mixed workloads (streaming low-latency and large batch analytics). Cover compute choices (reserved vs on-demand vs spot), serverless options, right-sizing, storage lifecycle (hot/cold tiers), and techniques to limit warehouse query costs (clustering, materialized views, sampling).
MediumTechnical
0 practiced
Given a raw_events table schema below, write a BigQuery SQL query that deduplicates events by event_id and assembles one row per ride_id with start_time and end_time. Handle late-arriving start/end events by taking the earliest start and latest end. Schema:Produce: ride_id, start_time, end_time, duration_seconds. Explain your deduplication approach.
raw_events(ride_id STRING, event_id STRING, event_type STRING, occurred_at TIMESTAMP, payload JSON)MediumTechnical
0 practiced
For storing ride events in object storage (S3/GCS) using Parquet, design a partitioning and file layout strategy that balances query performance and the small-files problem. Discuss partition keys, file sizing targets, compaction frequency, hive-style partitions vs partition columns, and how to handle queries filtered by date and city.
HardTechnical
0 practiced
Design a geospatial data model to support multi-zoom-level analytics at Lyft: city-wide heatmaps, neighborhood aggregations, and route-level analysis. Include H3 indexing strategy, storage of zone geometries, pre-aggregations for common tiles, and approaches for spatial joins and indexing in warehouses that support GEOGRAPHY types.
EasyTechnical
0 practiced
Explain H3 hexagonal geospatial indexing and how it can be used to aggregate rides by geographic area for Lyft. Describe how to choose resolution levels for city-level vs neighborhood-level analytics and the trade-offs between precision and storage/query performance.
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