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.

EasyTechnical
57 practiced
You're advising engineers on partition design for a fact table that grows by 1TB/day. What factors should guide the choice of partition key (date vs customer vs region vs composite), and what trade-offs affect query performance, maintenance, and data skew?
MediumTechnical
43 practiced
Compare row-oriented and columnar storage formats for analytics. Given a 1TB dataset used primarily for aggregations and ad-hoc reporting, recommend among Parquet, ORC, Avro, and CSV, and justify your choice regarding compression, predicate pushdown, and update patterns.
EasyTechnical
41 practiced
Explain the star schema pattern and why BI teams use it. Describe fact and dimension tables, surrogate keys, denormalization benefits, and how this design influences aggregation performance. Provide a concise e-commerce example (orders/order_items fact and product, customer dimensions).
MediumTechnical
41 practiced
A weekly revenue dashboard shows a 20% drop. Walk through a reproducible investigative process to determine whether the issue originates in source systems, ingestion, transformations, dimension joins, or the dashboard itself. Mention concrete checks, SQL queries and logs you'd run.
MediumSystem Design
41 practiced
Design a materialized view or aggregated summary to accelerate weekly retention queries in Snowflake. Explain your choice of aggregation grain, refresh strategy (incremental vs full), storage and compute trade-offs, and how the BI tool should reference the materialized object.

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.