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

Before dropping a widely-used column from a dimension table, outline an impact analysis and change plan: how to use lineage to find affected dashboards, tests to run to validate calculations, a staged deprecation process, and the communication plan to inform BI consumers and rollback if issues arise.

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.

MediumTechnical

0 practiced

As a BI Analyst you need to support dashboards when upstream teams frequently add/remove columns. Explain practical schema-evolution patterns supported by Parquet + table formats (Iceberg/Delta) such as add column, rename, type promotion, and dropping columns. Describe how to handle backfills, nullability, and version compatibility so dashboards continue to function.

HardSystem Design

0 practiced

Design an approach to keep KPI materialized views updated with <5s latency using streaming aggregation and incremental updates. Include components (streaming source, aggregation engine, sink to serving store), consistency model, failure recovery, and how to validate that KPIs are accurate under reprocessing/retries.

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