Download Explicit cursors

JRE SCHOOL OF Engineering
Solutions
Subject Name DBMS & DWM
Subject Code
For EE-VI Sem. only
SECTION – A
ECS-019
Q1. Explain aggregate functions in SQL.
The SQL Aggregate Functions are functions that provide mathematical operations.
If you need to add, count or perform basic statistics, these functions will be of great help.
The functions include:




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count() - counts a number of rows
sum() - compute sum
avg() - compute average
min() - compute minimum
max() - compute maximum
Q2. What is data mart? Explain its role in data warehousing.
A data mart is a simple form of a data warehouse that is focused on a single
subject (or functional area), such as Sales, Finance, or Marketing. Data marts are often
built and controlled by a single department within an organization. Given their singlesubject focus, data marts usually draw data from only a few sources. The sources could be
internal operational systems, a central data warehouse, or external data.
Each Data Mart can contain different combinations of tables, columns and rows
from the Enterprise Data Warehouse. For example, an business unit or user group that
doesn't require a lot of historical data might only need transactions from the current
calendar year in the database. The Personnel Department might need to see all details
about employees, whereas data such as "salary" or "home address" might not be
appropriate for a Data Mart that focuses on Sales.
SECTION – B
Q1. Discuss the differences between data warehouse and database system.
Database
Data Warehouse
Any collection of data organized for A type of database that integrates
storage, accessibility, and retrieval. copies of transaction data from
disparate source systems and
provisions them for analytical use.
There are different types of
databases, but the term usually
applies to an OLTP application
database, which we’ll focus on
throughout this table. Other types of
databases include OLAP (used for
A data warehouse is an OLAP
database. An OLAP database layers on
top of OLTPs or other databases to
perform analytics. An important side
note about this type of database: Not
all OLAPs are created equal. They
Database
Data Warehouse
data warehouses), XML, CSV files,
flat text, and even Excel
spreadsheets. We’ve actually found
that many healthcare organizations
use Excel spreadsheets to perform
analytics (a solution that is not
scalable).
differ according to how the data is
modelled. Most data warehouses
employ either an enterprise or
dimensional data model, but at Health
Catalyst, we advocate a unique,
adaptive Late- Binding™ approach.
You can learn more about why the
Late-Binding™ approach is so
important in healthcare analytics in
Late-Binding vs. Models: A
Comparison of Healthcare Data
Warehouse Methodologies.
Typically constrained to a single
application: one application equals
one database. An EHR is a prime
example of a healthcare application
that runs on an OLTP database.
OLTP allows for quick real-time
transactional processing. It is built
for speed and to quickly record one
targeted process (ex: patient
admission date and time).
Accommodates data storage for any
number of applications: one data
warehouse equals infinite applications
and infinite databases. OLAP allows
for one source of truth for an
organization’s data. This source of
truth is used to guide analysis and
decision-making within an
organization (ex: total patients over
age 18 who have been readmitted, by
department and by month).
Interestingly enough, complex queries
like the one just described are much
more difficult to handle in an OLTP
database.
Q2. Explain cursors in SQL with an example.
A cursor is a temporary work area created in the system memory when a SQL statement
is executed. A cursor contains information on a select statement and the rows of data
accessed by it.
This temporary work area is used to store the data retrieved from the database, and
manipulate this data. A cursor can hold more than one row, but can process only one row
at a time. The set of rows the cursor holds is called the active set.
There are two types of cursors in PL/SQL:
Implicit cursors
These are created by default when DML statements like, INSERT, UPDATE, and
DELETE statements are executed. They are also created when a SELECT statement that
returns just one row is executed.
Explicit cursors
They must be created when you are executing a SELECT statement that returns more than
one row. Even though the cursor stores multiple records, only one record can be
processed at a time, which is called as current row. When you fetch a row the current row
position moves to next row.
Both implicit and explicit cursors have the same functionality, but they differ in the way
they are accessed.
For Example: Consider the PL/SQL Block that uses implicit cursor attributes as shown
below:
DECLARE var_rows number(5);
BEGIN
UPDATE employee
SET salary = salary + 1000;
IF SQL%NOTFOUND THEN
dbms_output.put_line('None of the salaries where updated');
ELSIF SQL%FOUND THEN
var_rows := SQL%ROWCOUNT;
dbms_output.put_line('Salaries for ' || var_rows || 'employees are updated');
END IF;
END;
Q3. Describe the structure of a data warehouse with the help of a diagram.
•
The central data warehouse database is a cornerstone of data warehousing
environment
•
These approaches include the following:
–
Parallel relational database designs that require a parallel computing
platform
–
An innovative approach to speed up a traditional RDBMS by using new
index structures to bypass relational table scans
–
Multidimensional database (MDDBs) that are based on proprietary
database technology or implemented using already familiar RDBMS.
Multidimensional database are designed to overcome any limitations
placed on the warehouse by the nature of the relational data model
•
A significant portion of the data warehouse implementation effort is spent
extracting data from operational systems and putting it in a format suitable for
informational applications that will run off the data warehouse
•
Metadata is data about data that describes the data warehouse.
•
It is used for building, maintaining, managing, and using the data warehouse.
•
Metadata can be classified into the following:
–
Technical metadata
–
Business metadata
–
Data warehouse operational information such as data history (snapshots,
versions), ownership, extract audit trail, usage data
Most organizations engage in data mining to do the same following:
–
Discovering knowledge: segmentation, classification, association and
preferencing.
–
Visualizing Data
–
Correct data
SECTION – C
Q1. Specify the following queries in SQL based on given schema
Suppliers(sid,sname,city)
Part(pid,pname,color)
Sp(sid,pid,quantity)
i. Get the names of suppliers whose name begins with R
ii. Get pairs of supplier numbers that both operate from same city.
iii. Get the names of suppliers who supply part P2.
iv. Get the supplier id and names of suppliers in descending order of city.
v. Get the part numbers and total quantity supplied.
(i)select sname from supplier where sname like ‘k%’.
(ii)select s1.sid as sid1,s2.sid as sid2 from supplier s1,supplier s2 where
s1.city = s2.city and s1.sid<s2.sid.
(iii)select sname from supplier ,Part,Sp where Supplier.sid = sp.sid AND
sp.pid = Part.pid AND Part.pid =p2.
(iv) select sid,sname from supplier order by city desc.
(v) select Pname ,quantity from Sp s,Part p where s.pid=p.pid.
Q2. What are the differences between the three main types of data warehouse usage:
information processing, analytical processing and data mining? Briefly explain.
Data Mining:-Generally, data mining (sometimes called data or knowledge discovery) is
the process of analyzing data from different perspectives and summarizing it into useful
information - information that can be used to increase revenue, cuts costs, or both. Data
mining software is one of a number of analytical tools for analyzing data. It allows users
to analyze data from many different dimensions or angles, categorize it, and summarize
the relationships identified. Technically, data mining is the process of finding correlations
or patterns among dozens of fields in large relational databases.
Analytical Processing:-OLAP (online analytical processing) is computer processing that
enables a user to easily and selectively extract and view data from different points of
view. For example, a user can request that data be analyzed to display a spreadsheet
showing all of a company's beach ball products sold in Florida in the month of July,
compare revenue figures with those for the same products in September, and then see a
comparison of other product sales in Florida in the same time period. To facilitate this
kind of analysis, OLAP data is stored in a multidimensional database. Whereas a
relational database can be thought of as two-dimensional, a multidimensional database
considers each data attribute (such as product, geographic sales region, and time period)
as a separate "dimension." OLAP software can locate the intersection of dimensions (all
products sold in the Eastern region above a certain price during a certain time period) and
display them. Attributes such as time periods can be broken down into subattributes.
OLAP can be used for data mining or the discovery of previously undiscerned
relationships between data items. An OLAP database does not need to be as large as a
data warehouse, since not all transactional data is needed for trend analysis. Using Open
Database Connectivity (ODBC), data can be imported from existing relational databases
to create a multidimensional database for OLAP.
Information Processing:•
•
The functionality includes:
– Removing unwanted data from operational databases
– Converting to common data names and definitions
– Calculating summaries and derived data
– Establishing defaults for missing data
– Accommodating source data definition changes
The data sourcing, cleanup, extract, transformation and migration tools have to
deal with some significant issues, as follows:
– Database heterogeneity.
– Data heterogeneity.
Q3. Consider the following relational schema:Student(name,roll_number,address,main)
Admission(roll_no,course,semester)
Faculty(course,faculty,semester)
Offering(branch,course)
Assume suitable assumption if you need and write the following queries in SQL:i. The names of the students admitted in a particular course in a given semester.
ii. Students who have taken all the courses offered by the faculty ‘Ms. Sheela’.
iii. Name all the faculty who had taught student ‘Abhishek’.
iv. All courses taken by student ‘Asim’.
v. Find the names of all students who are studying same courses.
i. Select name,course,semester from student as s
Inner join admission as a where s.roll_number =a.roll_no;
ii. Select name from student where roll_number IN (Select roll_no from
admission as a
Inner Join faculty f where faculty=’Ms. Sheela’ );
iii. Select faculty from Faculty where course IN(select course from Admission
where roll_no IN(Select roll_number from student where
name=’abhishek’)));
iv. Select course from Admission where roll_no IN (select roll_number from
student where name=’Asim’);
v. Select
name
from
student
join
Admission
on
student.roll_numer=Admission.roll_no Group By course;