Cloud Data Warehouse Architecture Questions
Understand modern cloud data platforms: Snowflake, BigQuery, Redshift, Azure Synapse. Know their architecture, scalability models, performance characteristics, and cost optimization strategies. Discuss separation of compute and storage, time travel, and zero-copy cloning.
HardTechnical
0 practiced
Write an optimized SQL (target platform-agnostic) to deduplicate streaming events where events may arrive out-of-order. Schema:Keep the event with the latest event_ts; if event_ts equal, keep latest ingestion_ts. Consider window functions and partitioning strategy for performance on large tables.
events(id STRING, user_id STRING, event_ts TIMESTAMP, ingestion_ts TIMESTAMP, payload JSON)MediumTechnical
0 practiced
Explain how partitioning and clustering choices differ between BigQuery and Snowflake. For a time-series table of events, recommend partitioning and clustering settings in each platform to optimize a dashboard that filters by date and user_id.
EasyTechnical
0 practiced
What is data lineage and why is it important for BI teams using cloud data warehouses? Describe how metadata, automated tests, and documentation help maintain trustworthy dashboards.
MediumSystem Design
0 practiced
Design a simple CDC-to-warehouse flow using Kafka + Debezium + Snowflake (or BigQuery). Include how you would handle message ordering, exactly-once semantics or idempotence, schema changes, and how BI dashboards will read the updated data.
HardTechnical
0 practiced
You are evaluating moving a BI dataset from BigQuery to Snowflake. List the functional and non-functional criteria you would evaluate (performance, concurrency, cost model, ecosystem integrations, data-sharing, tooling, operational overhead). Propose a pilot strategy to compare them.
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