Cloud Data Warehouse Design and Optimization Questions

Covers design and optimization of analytical systems and data warehouses on cloud platforms. Topics include schema design patterns for analytics such as star schema and snowflake schema, purposeful denormalization for query performance, column oriented storage characteristics, distribution and sort key selection, partitioning and clustering strategies, incremental loading patterns, handling slowly changing dimensions, time series data modeling, cost and performance trade offs in cloud managed warehouses, and platform specific features that affect query performance and storage layout. Candidates should be able to discuss end to end design considerations for large scale analytic workloads and trade offs between latency, cost, and maintainability.

HardTechnical

0 practiced

Explain how compression, zone maps, and micro-partitions work in cloud warehouses like Snowflake or columnar engines, and how they influence predicate pushdown and I/O pruning. Provide examples of how good clustering or sorting can reduce IO by orders of magnitude.

MediumSystem Design

0 practiced

Design a star-schema for time-series metrics ingested at 100M events/day. The main queries are by time range (hour/day), region, and metric type. Propose table schemas and explain partitioning and clustering strategy for BigQuery or Snowflake to support efficient range scans and aggregations.

MediumTechnical

0 practiced

You have a dataset written in Parquet consumed by analytics. Describe strategies to handle schema evolution (new columns, type changes, renamed fields) without breaking downstream queries or pipelines. Discuss use of Avro/Protobuf evolution strategies, schema registries, and table-level compatibility checks.

MediumTechnical

0 practiced

Compare using materialized views (or search-optimized precomputed tables) versus creating scheduled pre-aggregation ETL jobs. When would you use materialized views provided by the cloud warehouse product, and when are bespoke pre-aggregation tables preferable?

EasyTechnical

0 practiced

Explain the key characteristics of columnar (column-oriented) storage and why columnar formats are preferred for analytics. Cover compression possibilities, IO patterns, predicate pushdown, vectorized execution, and the impact of wide vs narrow tables on performance and storage.

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