Data warehouse vs data lake in HLD - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key difference in data storage format

Which option correctly describes the primary difference in data storage format between a data warehouse and a data lake?

A Data warehouses store raw data; data lakes store only processed data.

B Data warehouses store structured data in tables; data lakes store raw data in its native format.

C Data warehouses store unstructured data; data lakes store only structured data.

D Data warehouses and data lakes both store data only in relational databases.

💡 Hint

Think about how data is organized and processed in each system.

Data warehouses organize data in structured tables optimized for queries. Data lakes keep raw data as-is, including unstructured formats.

❓ Architecture

intermediate

Choosing between data warehouse and data lake for analytics

A company wants to perform fast business reporting on cleaned sales data and also explore raw customer interaction logs for insights. Which architecture best supports both needs?

A Use a data lake to store raw logs and a data warehouse for cleaned sales data and reporting.

B Use only a data warehouse for all data storage and analytics.

C Use only a data lake for all data storage and analytics.

D Store all data in flat files on local servers without any specialized system.

💡 Hint

Consider the strengths of each system for different data types and use cases.

Data lakes handle raw, unstructured data well for exploration. Data warehouses excel at fast queries on cleaned, structured data.

❓ scaling

advanced

Scaling storage for growing data volumes

A company’s data volume is growing rapidly with diverse data types. Which scaling approach best suits a data lake compared to a data warehouse?

A Both data lakes and data warehouses scale only by adding more servers manually.

B Data warehouses scale easily by adding cheap storage; data lakes require expensive compute upgrades.

C Neither data lakes nor data warehouses can scale beyond a few terabytes.

D Data lakes scale easily by adding cheap storage; data warehouses require expensive scaling of compute and storage.

💡 Hint

Think about cost and flexibility of storage expansion in each system.

Data lakes use low-cost storage that can grow easily. Data warehouses often need costly compute and storage upgrades to scale.

❓ tradeoff

advanced

Tradeoff between data governance and flexibility

Which statement best describes the tradeoff between data governance and flexibility when choosing between a data warehouse and a data lake?

A Both data warehouses and data lakes have identical governance and flexibility levels.

B Data lakes enforce strict governance automatically; data warehouses have no governance controls.

C Data warehouses enforce strict governance but limit flexibility; data lakes offer flexibility but require more governance effort.

D Data lakes have no governance and no flexibility; data warehouses have full flexibility without governance.

💡 Hint

Consider how each system manages data quality and access controls.

Data warehouses have structured schemas and controls for governance. Data lakes allow raw data but need extra effort to govern properly.

❓ estimation

expert

Estimating query performance impact

A data warehouse and a data lake both store 10 TB of data. Which factor most impacts query performance difference between them?

A Data warehouse uses optimized indexing and pre-structured data; data lake queries often scan large raw files.

B Data lake uses indexing and pre-structured data; data warehouse queries scan raw files.

C Both systems have identical query speeds regardless of data format.

D Query performance depends only on network speed, not data format or structure.

💡 Hint

Think about how data organization affects how fast queries run.

Data warehouses optimize queries with indexes and schemas. Data lakes often require scanning large raw datasets, slowing queries.