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Data Lake and Warehouse Architecture Questions

Designing scalable data platforms for analytical and reporting workloads including data lakes, data warehouses, and lakehouse architectures. Key topics include storage formats and layout including columnar file formats such as Parquet and table formats such as Iceberg and Delta Lake, partitioning and compaction strategies, metadata management and cataloging, schema evolution and transactional guarantees for analytical data, and cost and performance trade offs. Cover ingestion patterns for batch and streaming data including change data capture, data transformation approaches and compute engines for analytical queries, partition pruning and predicate pushdown, query optimization and materialized views, data modeling for analytical workloads, retention and tiering, security and access control, data governance and lineage, and integration with business intelligence and real time analytics. Also discuss operational concerns such as monitoring, vacuuming and compaction jobs, metadata scaling, and strategies for minimizing query latency while controlling storage cost.

MediumTechnical
66 practiced
Write a SQL MERGE statement (Snowflake/Delta syntax) that incrementally updates a daily summary table from a staging_events table. Staging schema: (user_id, event_date DATE, revenue DOUBLE). Summary table: (event_date, total_revenue DOUBLE, user_count INT). Ensure the statement handles inserts, updates, and avoids double counting. Include notes on performance considerations for large daily batches.
MediumTechnical
72 practiced
Describe steps to implement dynamic row-level security (RLS) for Power BI connected to a SQL-based analytics warehouse such that users see only their region's data. Include how you'd implement the security predicate in the warehouse, pitfalls in caching/extracts, and how to test and validate access control.
HardTechnical
79 practiced
You observe a 3x increase in monthly BI query costs without proportional user growth. As a BI Analyst with access to cost and usage reports, outline an investigation plan and a prioritized list of optimizations (compute sizing, query tuning, materialized views, caching, schedule/report extract usage) to reduce cost while preserving SLA for core dashboards.
MediumTechnical
60 practiced
Explain the ACID/transactional guarantees provided by Delta Lake, Iceberg, and Hudi. For a near-real-time executive dashboard that must not show partially written aggregates, which guarantees are required and how do these table formats provide them? Also discuss implications of isolation levels for reading during ongoing writes and compactions.
MediumTechnical
69 practiced
Materialized views can speed up BI queries but add maintenance cost. Explain when to use materialized views vs scheduled aggregate tables vs on-the-fly aggregation. Discuss freshness guarantees, incremental maintenance, storage cost, query routing, and failure modes across systems like Snowflake, BigQuery, and Databricks.

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