ALCCS
NOTE:
· Question 1 is compulsory and
carries 28 marks. Answer any FOUR questions from the rest. Marks are indicated against each question.
· Parts of a question should be
answered at the same place.
Q.1 (7
4)
a. Discuss the four levels of
data in the architected environment.
b. Discuss the problem related to use and
storage of unstructured data in the data warehouse.
c. Discuss the three
types of distributed Data Warehouse?
d. Compare and contrast the system development
life cycle for data warehouse with the classical SDLC.
e. Write
four techniques that can be used to limit the amount of operational data
scanned at the point of refreshing the data warehouse.
f. Discuss
the role of metadata in a Data Warehouse Environment.
g. Define
the following terms:
(i) Business
Metadata.
(ii) Technical Metadata.
(iii) Index Only Processing.
(iv) Fast Restore
Q.2 a. A data warehouse
is a subject-oriented, integrated, time-variant and non-volatile collection of
data to support of management’s decision-making process. Comment?
b. How is data structured in a Data Warehouse?
Explain?
c. What is Granularity? What are its benefits related
to a Data Warehouse? (8+6+4)
Q.3 a. Write short note on
(i)
Techniques
to make feedback loop harmonious.
(ii) Snapshots in Data
Warehouse.
b. Write in detail about the three data models
used in Data Warehouse. (4+4+10)
Q.4 a. Explain Star Schema and snowflake schema with
the help of examples.
b. Discuss the
technological requirements of a Data Warehouse. (9+9)
Q.5 a. Differentiate
between
(i) Data Warehouse and MDBMS.
(ii) OLAP and OLTP.
b. What is a Multidimensional DBMS? Discuss the advantages and
disadvantages of relational foundation of multidimensional DBMS and cube foundation
of multidimensional DBMS. (5+5+8)
Q.6 a. Discuss the
architecture of a data warehouse with the help of a diagram.
b. Explain Drill-Down
Analysis and Event Mapping in context of EIS. (8+10)
Q.7 Write a short note on any THREE: (6+6+6)
(i) Partitioning of Data in Data Warehouse.
(ii) Complexities in transformation and
integration of data.
(iii) Global and Local Data Warehouse.
(iv) Data Marts.