Data warehouse vs data lake in HLD - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to identify the main storage type of a data warehouse.

HLD

A data warehouse primarily stores data in a [1] format optimized for analysis.

Drag options to blanks, or click blank then click option'

Astructured

Bunstructured

Craw

Dbinary

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing unstructured because of confusion with data lakes.

A data warehouse stores data in a structured format, which means data is organized and formatted for easy querying and analysis.

2fill in blank

medium

Complete the code to identify the type of data stored in a data lake.

HLD

A data lake stores [1] data, including structured, semi-structured, and unstructured formats.

Drag options to blanks, or click blank then click option'

Aonly structured

Ball types of

Conly semi-structured

Donly unstructured

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting only one data type instead of all types.

Data lakes store all types of data: structured, semi-structured, and unstructured, allowing for flexible data ingestion.

3fill in blank

hard

Fix the error in the statement about data processing in data warehouses.

HLD

Data warehouses require data to be [1] before storage to ensure quality and consistency.

Drag options to blanks, or click blank then click option'

Acompressed

Bencrypted

Ccleaned

Dindexed

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing cleaning with encryption or compression.

Data warehouses require data to be cleaned (processed and transformed) before storage to maintain quality and consistency.

4fill in blank

hard

Fill both blanks to describe the main difference in schema handling between data warehouses and data lakes.

HLD

Data warehouses use [1] schema, while data lakes use [2] schema.

Drag options to blanks, or click blank then click option'

Aschema-on-write

Bschema-on-read

Cflat

Dnested

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up schema-on-write and schema-on-read.

Data warehouses use schema-on-write, meaning data is structured before storing. Data lakes use schema-on-read, meaning data is structured when read.

5fill in blank

hard

Fill all three blanks to complete the comparison of data access and cost between data warehouses and data lakes.

HLD

Data warehouses provide [1] access with [2] cost, while data lakes offer [3] access with lower cost.

Drag options to blanks, or click blank then click option'

Afast

Bhigh

Cflexible

Dslow

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing access speed or cost attributes between the two.

Data warehouses provide fast access but at a higher cost due to optimized storage and processing. Data lakes offer flexible access to various data types at a lower cost.

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