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).
HardSystem Design
0 practiced
Propose a governance model and technical controls for column-level PII masking in Lyft's analytics platform that support both automated masking for general analysts and controlled unmasking for authorized investigators. Include implementation options (tokenization, dynamic masking, query rewriting), role-based access controls, audit logging, and integration with the data catalog.
MediumSystem Design
0 practiced
Design a geospatial tiling and aggregation strategy to support map-based heatmaps of pick-up density at multiple zoom levels for Lyft. Cover tile scheme choices (S2 vs quadkey), pre-aggregation cadence (per minute/hour/day), storage layout for tile-rollups, cache/CDN strategy for serving tiles to BI dashboards, and approaches to incremental updates for near-real-time displays.
MediumSystem Design
0 practiced
Propose how to capture and surface end-to-end data lineage and metadata for trip facts and driver dimensions so analysts can perform impact analysis and audits. Specify which metadata to capture (dataset versions, transformation SQL, schedule/run_id), where to store lineage, and how BI tools should be integrated to show upstream/downstream dependencies.
HardSystem Design
0 practiced
Architect a system to power a driver-facing real-time earnings dashboard updated every minute with strong consistency. Specify streaming components, state stores for per-driver aggregates, idempotent write patterns, ordering guarantees, caching strategies for the frontend, and how to reconcile partial failures or network partitions to maintain correct earnings presentation.
MediumSystem Design
0 practiced
Design a trip-level analytic schema to support Lyft hourly operational dashboards and cohort analysis. Provide a proposed fact table 'fact_trips' with key columns and at least three dimension tables (driver, rider, location). Explain partitioning strategy (e.g., by started_at date), clustering keys (e.g., city, driver_id), retention policy, and how the design supports fast aggregations for high-cardinality queries.
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