Data Model Design and Access Patterns Questions
Discuss how you'd design data models based on access patterns. Understand relational vs. NoSQL trade-offs. Know when to denormalize, how to handle distributed transactions, and strategies for scaling databases (sharding, partitioning). Discuss read vs. write optimization.
EasyTechnical
40 practiced
Describe denormalization: typical patterns (duplicated columns, precomputed aggregates, embedding joined data), reasons to denormalize for read-heavy workloads, and the trade-offs this introduces for write amplification, storage, and correctness. When would you avoid denormalization?
MediumTechnical
32 practiced
Design a hybrid system where payments require strong consistency and reporting can be eventual. Describe the transactional data model for payments, the event capture pipeline to feed a reporting store, and strategies to ensure reports are eventually consistent while not impeding payment latency.
MediumTechnical
33 practiced
Explain the CQRS (Command Query Responsibility Segregation) pattern and when it makes sense. Provide a concrete example splitting a normalized write model and a denormalized read model (projections), explain how projections are updated reliably, and discuss the trade-offs in complexity, latency, and consistency.
HardTechnical
43 practiced
Model geospatial data to support proximity queries at scale (e.g., find nearest restaurants within 5 km). Discuss coordinate representation options (lat/long, geohash, S2 cells), indexing approaches (R-tree/GiST, geohash prefix), sharding and partitioning by geography, and techniques to minimize false positives and extra distance calculations.
MediumTechnical
39 practiced
Compare multi-tenant strategies: shared schema with tenant_id column, schema-per-tenant, and database-per-tenant. For each option discuss isolation/security, per-tenant backup/restore, resource throttling, and how access patterns (hot tenants) influence architecture and costs.
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