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

Design a bitemporal SCD Type 2 schema to support back-dated corrections and point-in-time analysis for legal/regulatory reporting. Include table structures with business_time (valid_from/valid_to) and system_time (sys_from/sys_to), primary keys, indexing/clustering, and how queries are written to retrieve the record state at an arbitrary business time. Discuss storage cost and query performance implications.

EasyTechnical

0 practiced

Explain the purpose of incremental loading into a cloud data warehouse. Describe common patterns (watermark/timestamp-based, Change Data Capture (CDC), delta merges) and key considerations to ensure idempotency, correct ordering, and eventual consistency across dependent aggregates.

MediumTechnical

0 practiced

Explain Snowflake micro-partitions and how clustering keys affect pruning and query performance. Provide guidance on when to define clustering keys manually, how to choose columns, and how to measure clustering effectiveness and maintenance cost.

MediumTechnical

0 practiced

You observe a table scanning many terabytes mostly due to a wide varchar column with many repeated values and many nulls. Propose compression/encoding and schema-change strategies (e.g., dictionary encoding, surrogate ids, nullable handling) and describe how to measure and validate improvement in a columnar warehouse.

MediumTechnical

0 practiced

Design a star schema for an e-commerce analytics workload and provide CREATE TABLE DDLs in SQL targeted to Snowflake or Redshift. Given a fact 'orders' (order_id, customer_id, order_ts, total_amount, promotion_id) and dimensions customers, products, promotions, and time, include surrogate keys, approximate data types, and indicate distribution/sort or clustering choices.

Unlock Full Question Bank

Get access to hundreds of Cloud Data Warehouse Design and Optimization interview questions and detailed answers.

Join thousands of developers preparing for their dream job.