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

0 practiced

Design a set of automated data quality checks appropriate for critical dashboards: include row-count checks, null-rate thresholds, referential integrity checks, distributional checks, and freshness checks. Suggest technologies (e.g., Great Expectations, custom SQL) and how BI teams should prioritize checks (which metrics are critical vs optional).

EasyTechnical

0 practiced

As a BI Analyst, list dashboard development guidelines that reduce load on the data platform: e.g., limit visuals using high-cardinality fields, use parameterized filters, avoid unbounded COUNT(DISTINCT), use extracts for heavy dashboards, and recommend default date ranges. Explain why each guideline improves performance.

HardTechnical

0 practiced

Explain how vectorized query execution and columnar caches (e.g., runtime vectorized processing on Parquet column chunks) reduce CPU and IO for analytical queries. Discuss how these engine-level optimizations influence decisions on file size, row-group size, column ordering, and compression choices for datasets powering BI dashboards.

EasyTechnical

0 practiced

List the key monitoring metrics and alerts you would set up to ensure data pipelines and analytical tables used by BI are healthy (e.g., freshness lag, load failures, row-count deltas, schema drift, compaction failures). As a BI Analyst, describe how you would act on an alert showing a sudden row-count drop for yesterday's data.

MediumTechnical

0 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.

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