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.

Practice

1. What is the main difference between a data warehouse and a data lake?

easy

A. Data warehouse and data lake are exactly the same.

B. Data warehouse stores raw data; data lake stores only images.

C. Data warehouse is for storing videos; data lake is for storing text.

D. Data warehouse stores structured data; data lake stores raw data.

Solution

1. Step 1: Understand data warehouse data type

   A data warehouse stores cleaned and structured data optimized for fast queries and reporting.

2. Step 2: Understand data lake data type

   A data lake stores raw, unprocessed data in various formats, allowing flexible analysis later.

3. Final Answer:

   Data warehouse stores structured data; data lake stores raw data. -> Option D

4. Quick Check:

   Data warehouse = structured, Data lake = raw [OK]

Hint: Data warehouse = clean data; data lake = raw data [OK]

Common Mistakes:

• Confusing raw data with structured data
• Thinking both store only structured data
• Assuming data lakes store only images or videos

2. Which of the following is a correct statement about data lakes?

easy

A. Data lakes only store data in relational tables.

B. Data lakes can store structured, semi-structured, and unstructured data.

C. Data lakes require data to be cleaned before storing.

D. Data lakes are only used for real-time data processing.

Solution

1. Step 1: Review data lake storage capabilities

   Data lakes store all types of data: structured, semi-structured (like JSON), and unstructured (like images).

2. Step 2: Check other options

   Data lakes only store data in relational tables. is false because data lakes do not require relational tables. Data lakes require data to be cleaned before storing. is false because data lakes store raw data without cleaning. Data lakes are only used for real-time data processing. is false as data lakes are not limited to real-time processing.

3. Final Answer:

   Data lakes can store structured, semi-structured, and unstructured data. -> Option B

4. Quick Check:

   Data lakes = all data types [OK]

Hint: Data lakes store all data types, not just tables [OK]

Common Mistakes:

• Thinking data lakes only store tables
• Assuming data must be cleaned before storing
• Believing data lakes are only for real-time data

3. Consider a company that wants to analyze customer behavior using both structured sales data and raw clickstream logs. Which architecture best fits this need?

medium

A. Store all data in a data warehouse only.

B. Store clickstream logs in a data warehouse and sales data in a data lake.

C. Store sales data in a data warehouse and clickstream logs in a data lake.

D. Store all data in a data lake only.

Solution

1. Step 1: Match data types to storage

   Structured sales data fits well in a data warehouse for fast reporting. Raw clickstream logs fit better in a data lake for flexible analysis.

2. Step 2: Evaluate options

   Store sales data in a data warehouse and clickstream logs in a data lake. correctly assigns sales data to warehouse and logs to lake. Options A and C use only one storage type, which is less efficient. Store clickstream logs in a data warehouse and sales data in a data lake. reverses the ideal storage.

3. Final Answer:

   Store sales data in a data warehouse and clickstream logs in a data lake. -> Option C

4. Quick Check:

   Warehouse = structured, Lake = raw logs [OK]

Hint: Structured data in warehouse, raw logs in lake [OK]

Common Mistakes:

• Putting all data in one storage type
• Mixing raw logs into warehouse
• Reversing data types and storage

4. A team designed a system storing all raw data in a data warehouse without cleaning it first. What is the main problem with this design?

medium

A. Data warehouses cannot store raw data efficiently.

B. Data warehouses automatically clean raw data, so no problem.

C. Data lakes require data to be cleaned before storing.

D. Raw data is faster to query in data warehouses.

Solution

1. Step 1: Understand data warehouse requirements

   Data warehouses expect cleaned, structured data for fast querying and reporting.

2. Step 2: Identify issue with raw data in warehouse

   Storing raw data in a warehouse causes inefficiency and poor query performance because it is not structured or cleaned.

3. Final Answer:

   Data warehouses cannot store raw data efficiently. -> Option A

4. Quick Check:

   Warehouse needs cleaned data [OK]

Hint: Raw data in warehouse causes inefficiency [OK]

Common Mistakes:

• Assuming warehouses clean data automatically
• Confusing data lake requirements
• Thinking raw data queries are faster in warehouses

5. A company wants to build a system that supports fast business reporting and also flexible data science experiments on raw data. Which design is best?

hard

A. Use a data warehouse for reporting and a data lake for raw data experiments.

B. Use only a data lake for all data storage and analysis.

C. Use only a data warehouse for all data storage and analysis.

D. Use a data lake for reporting and a data warehouse for raw data.

Solution

1. Step 1: Identify reporting needs

   Fast business reporting requires cleaned, structured data in a data warehouse.

2. Step 2: Identify data science needs

   Flexible experiments need raw, diverse data stored in a data lake.

3. Step 3: Combine storage types

   Using both a data warehouse for reporting and a data lake for raw data experiments meets both needs efficiently.

4. Final Answer:

   Use a data warehouse for reporting and a data lake for raw data experiments. -> Option A

5. Quick Check:

   Warehouse = reporting, Lake = experiments [OK]

Hint: Combine warehouse for reports and lake for raw data [OK]

Common Mistakes:

• Using only one storage type for all needs
• Reversing roles of warehouse and lake
• Ignoring data science flexibility requirements