InterviewStack.io LogoInterviewStack.io

Data Warehousing and Data Lakes Questions

Covers conceptual and practical design, architecture, and operational considerations for data warehouses and data lakes. Topics include differences between warehouses and lakes, staging areas and ingestion patterns, schema design such as star schema and dimensional modeling, handling slowly changing dimensions and fact tables, partitioning and bucketing strategies for large datasets, common architectures including medallion architecture with bronze silver and gold layers, real time and batch ingestion approaches, metadata management, and data governance. Interview questions may probe trade offs between architectures, how to design schemas for analytical queries, how to support both analytical performance and flexibility, and how to incorporate lineage and governance into designs.

MediumTechnical
0 practiced
Design a strategy to support analytical queries on a fact table with very high-cardinality dimensions. Discuss denormalization vs normalized joins, the use of materialized views, pre-aggregation, and practical tips specific to BigQuery or Snowflake to balance performance and cost.
HardSystem Design
0 practiced
You must implement a production CDC pipeline from PostgreSQL to your data warehouse using Debezium into Kafka, a stream processing layer, and then load into Snowflake or BigQuery. Explain how you will ensure exactly-once semantics, handle schema evolution and DDL, and perform safe backfills when the sink is temporarily unavailable.
EasyTechnical
0 practiced
What is Change Data Capture (CDC) and why is it useful for analytics and model training? Describe two common CDC implementations (log-based CDC and trigger-based) and the pros/cons of each for feeding a data lakehouse.
HardTechnical
0 practiced
Your datasets are partitioned by ingestion_date, but many analytical queries filter on event_time that may be earlier than ingestion. Describe strategies to redesign storage layout, maintain efficient compaction, and enable partition pruning for queries filtering on event_time while avoiding excessive data duplication.
EasyTechnical
0 practiced
List common data quality checks you would implement at ingestion to a bronze layer. For each check (e.g., nulls, duplicate keys, out-of-range values, schema conformity) explain the expected action (reject, quarantine, tag) and the downstream impact on ML pipelines.

Unlock Full Question Bank

Get access to hundreds of Data Warehousing and Data Lakes interview questions and detailed answers.

Sign in to Continue

Join thousands of developers preparing for their dream job.