Data Modeling for Query Performance Questions

Focuses on schema and data modeling choices that enable efficient querying at scale. Topics include normalization and denormalization trade offs, analytical schemas such as star schema and snowflake schema, the roles of fact tables and dimension tables, modeling for common query patterns and aggregations, and how model choices impact indexing, join costs, and storage. Candidates should be able to justify schema decisions based on query workload, discuss partitioning and sharding implications for model design, and propose modeling adjustments that improve query latency and maintainability.

EasyTechnical

0 practiced

Explain common Slowly Changing Dimension (SCD) types (Type 0, 1, 2, 3) and describe the performance and storage implications of implementing SCD Type 2 for a customer dimension used in analytics. How does SCD Type 2 affect joins from facts to dimensions and what indexes or schemas help efficient historical joins?

MediumTechnical

0 practiced

Design a model to represent product hierarchies (category -> subcategory -> product) to support fast roll-up aggregations and easy maintenance. Compare approaches: denormalized hierarchy column (path), adjacency list (parent_id), nested set, and path enumeration. For each approach, discuss query performance for roll-ups and maintenance complexity when moving nodes.

HardSystem Design

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Architect a data warehouse schema for analytics where multiple dimension tables (for example, user_id and product_id) have extremely high cardinality (hundreds of millions of distinct values). Discuss encoding strategies (dictionary encoding, surrogate keys), sharding/distribution keys, bloom filters, partial denormalization, and the costs of materialized joins. How would you ensure acceptable join and aggregation performance?

HardTechnical

0 practiced

Design a clustering and partitioning strategy in a distributed columnar data store to maximize partition pruning and minimize I/O for complex analytical queries that commonly filter on date, country, and product_category. Provide an example DDL (columns and partition/clustering choices) and explain physical layout decisions, compaction/re-clustering policies, and maintenance tasks.

MediumTechnical

0 practiced

Compare schema-on-read (data lake) and schema-on-write (data warehouse) approaches from the perspective of data modeling and query performance. For analytical workloads that ingest semi-structured sources with frequent schema changes, explain when you'd choose schema-on-read versus schema-on-write and how each impacts model choices, query latency, and maintenance.

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