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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
Discuss strategies for schema evolution in a data lake used for analytics. How do you handle new columns, column renames, and incompatible type changes while preserving historical analysis and avoiding pipeline breaks?
HardTechnical
0 practiced
Explain partitioning strategies for time-series datasets with very high-cardinality dimensions such as device_id or customer_id. Discuss bucketing/hashing, multi-dimensional partitioning, and hybrid strategies, and include an example configuration or SQL partition scheme.
EasyTechnical
0 practiced
What is metadata and why is a data catalog important for an analyst? List at least three types of metadata (technical and business) you would expect for a core business table and explain how you'd use them when building reports.
HardTechnical
0 practiced
You are leading a cross-functional initiative to consolidate enterprise reporting into a single curated data model (gold layer). How do you align stakeholders, define data contracts, prioritize datasets for migration, and measure adoption and success? Describe governance and change-management steps you would take.
MediumTechnical
0 practiced
Given two tables:
customers(customer_id PK, signup_date date)orders(order_id PK, customer_id, order_date date, amount numeric)
Write a SQL query (PostgreSQL) to compute 3-month retention rate per monthly cohort and cohort-average revenue per customer. Explain assumptions for nulls and customers with no orders.

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