Database Architecture and Partitioning Questions

Design database architecture and partitioning strategies appropriate to workload and access patterns. Evaluate database types including relational and various NoSQL models, schema design and indexing strategies, and when to use a monolithic database versus sharding. Cover sharding approaches such as range based, hash based, consistent hashing, and directory based sharding, as well as replica topologies, read replicas, replication lag, and handling cross shard queries. Address operational concerns at scale: resharding, mitigating hot partitions, balancing data distribution, transactional and consistency guarantees, and the trade offs between availability, consistency, and partition tolerance. Include monitoring, migration strategies, and impact on application logic and joins.

MediumTechnical

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Explain the trade-offs between two-phase commit (2PC) and the saga pattern for multi-shard updates required by a fraud detection pipeline. As a data scientist, what consistency guarantees matter most for model correctness and what eventual consequences might each approach have on model inputs?

HardSystem Design

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You run a multi-tenant SaaS where tenant data is sharded by tenant_id. Design a sharding and backup/restore strategy that supports per-tenant restores (for compliance), tenant isolation for noisy neighbors, and efficient resource utilization. Include partitioning, backup granularity, and restoration steps.

EasyTechnical

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You're designing schemas for analytics: one table stores raw event streams (high cardinality JSON payload) and another stores daily user aggregates. As a data scientist, describe the schema and partitioning strategy for both tables and explain how they support iterative model development and backfills.

EasyTechnical

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Explain range-based sharding versus hash-based sharding. Provide one clear example workload where range-based sharding is better and one where hash-based sharding is better. As a data scientist, why would the choice matter for analytic queries and model fairness?

MediumTechnical

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You're asked to reduce cost of storing historical training data without losing the ability to re-run experiments. Propose a tiered storage and partitioning policy that supports: (1) fast access to recent months, (2) economical cold storage older data, and (3) the ability to restore cold data for re-training within reasonable time. Include formats and partitioning recommendations.

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