Data Warehousing (CCS341) model question paper

Data Warehousing (CCS341)

Model Question Paper
Course Title:Data Warehousing (CCS341)

Part A:  (2 x 10 = 20 Marks)


1. Compare operational databases and data warehouses.
2. Explain the architecture of a modern data warehouse.



3. Discuss the key differences between ETL and ELT.
4. Describe the characteristics and operations of OLAP

5. Explain the role of metadata in data warehousing.
6. Discuss the importance and design considerations of data marts.

7. Compare and contrast star schema and snowflake schema.
8. Explain the concept of dimensional modeling and its significance in data warehousing.

9. Describe the functions of a system configuration manager in a data warehousing system.
10. Explain the roles and responsibilities of a warehouse manager.

Part B:  (5 x 13 = 65 Marks)

11. (a) Explain the components of a data warehouse and their functions.
    (b) Describe the three-tier architecture of a data warehouse and its advantages.

12. (a) Discuss the design and modeling process of a data warehouse.  (B) Compare and contrast ROLAP, MOLAP, and HOLAP.

13. (a) Describe the challenges associated with metadata management in data warehousing.
    
    (b) Explain various partitioning strategies and their importance in data warehousing.

14. (a) Discuss the process architecture of a data warehouse and its significance.
   
    (b) Explain the different types of database parallelism and their applications in data warehousing.


15. (a) Describe the functions of system event manager and system backup recovery manager.
    
    (b) Explain the roles of load manager and query manager in data warehousing.

Part C: (1 x 15 = 15 Marks)



16. (a) Imagine you are tasked with designing a data warehouse for a retail company. Outline the steps involved in the ETL process, considering the specific needs of the retail industry. Discuss how you would handle data quality issues and ensure efficient data loading.

   

    (b) Given a dataset of sales transactions, design a star schema to support business analysis. Include fact and dimension tables with hypothetical data. Explain how this schema can be used to analyze sales performance and customer behavior.


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