Data Modeling and Architecture Questions

Design and modeling principles for transactional and analytical data systems. Topics include entity relationship modeling, normalization and denormalization trade offs, dimensional modeling with fact and dimension tables and star and snowflake schemata, indexing strategies, partitioning and sharding, and schema design for performance and maintainability. Cover data pipelines and integration patterns including extract transform load and extract load transform approaches, data warehousing and data lake concepts, ETL orchestration, and how sources feed into reporting and business intelligence systems. Also include considerations for data quality, governance, and the differences between online transaction processing and online analytical processing workloads.

HardTechnical

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Design a data model for a multi-tenant SaaS analytics platform where tenants may require both isolated storage and the ability for the provider to run cross-tenant aggregate reports. Compare options: separate databases per tenant, separate schemas, shared schema with tenant_id, and hybrid approaches. Discuss security, operational complexity, and performance trade-offs.

MediumTechnical

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When and how would you use materialized views in a data warehouse for reporting? Discuss refresh strategies (incremental vs full), cost and maintenance, staleness guarantees, and when materialized views are preferable to precomputed ETL tables.

EasyTechnical

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Explain entity-relationship (ER) modeling and its core components in the context of designing a transactional system for an e-commerce platform. Define entities, attributes, relationships, cardinality (1:1, 1:N, N:M), primary keys, foreign keys, and the purpose of normalization. As an example, describe (in words or a simple pseudo-diagram) how you would model Customers, Orders, OrderItems, and Products and how you'd enforce the many-to-many relationship between Orders and Products.

EasyTechnical

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Describe the goals of normalization (1NF, 2NF, 3NF) and provide concrete examples of normalization vs denormalization trade-offs. For a reporting table used in analytics (e.g., daily_sales_aggregate), when would you normalize and when would you denormalize? Explain the impact on query performance, storage, and ETL complexity.

EasyTechnical

0 practiced

Explain surrogate keys vs natural keys. For a data warehouse dimension like Customers, what are the advantages of using surrogate keys (integer/UUID) as the primary key? When might a natural key still be preferable? Include operational considerations (merging data, upstream changes).

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