SlideShare a Scribd company logo
Name - Shubham Pathania
Class - MCA (LEET)
UID - 19MCA8125
Subject - ADBMS (CAT-762)
Group - 4th
Submitted to Submitted by
Sandeep Kaur Shubham Pathania
INDEX
Sr No. Contents Page
no.
1 Compare and Contrast multi database system
with centralized system
1-2
2 Explain various factors that affect the selection
of DBMS software.
2-3
3 Check if a given a year is a leap year. The
condition is:-
Year should be (divisible by 4 and not divisible by
100) or (divisible by 4 and divisible by 400.)
Display the output on the screen using
dbms_output.put_line. The year should be input
by the user.
4
4 Ask the user to enter the weight of an apple
box. If the
weight is >= 10 kg, rate =Rs. 5/kg
weight is < 10 kg, rate = Rs. 7/kg
Calculate the cost of the apple box. Display the
output on the screen using
dbms_output.put_line
5
5 Program should accept the age of the user.
Depending upon the following conditions it
should output:-
age <18 years? Child?
Age >= 18 years and <21 years? Major?
Age >= 21years? Adult?
6
1. Compare and Contrast multi database system with centralized system?
Ans There are many aspect that let us make a comparison between centralized
and distributed DBMS:
 Database management system is any software that manages and
controls the storage, the organization, security, retrieval and integral of
data in a specific database, whereas DDBMS consist of a single database
that is divided into many fragments. Each fragment is integrated on one
or more computer and controlled by independent database (DBMS).
 In centralized DBMS the data is distributed across the network
computers, and the data is stored on many sites and under the
management responsibility of DDBMS. But in the DBMS data is stored
and controlled in a central site.
 Both of DDBMS and centralized DBMS provide the access to database
using the same interface, but for this function centralized DBMS faces
less complication than DDBMS.
 For distributing data over network we can use replication or
fragmentation. The objective of replication and fragmentation is to make
a transparency of this allocation to make the details of implementation
hidden on users. In centralized DBMS is not need to make
transparencies.
 In DDBMS design we can find three issues which are not in centralized
DBMS design.
 Consequently, centralized DBMS is less sophisticated than DDBMS
because it not supports the organizational structure of today’s widely
distributed enterprises, and DDBMS more reactive and reliable.
Difference -
Centralized DBMS Distributed DBMS
Data is stored only on one site Data is stored on different sites
Data stored in single computer can be
used by multiple users
Data is stored over different sites
which are connected with each
other.
If centralized system fails, then the
entire system is halted.
If one of the system fails, then user
can access the data from other sites.
Centralized DBMS Distributed DBMS
Centralized DBMS is less reliable and
reactive
Distributed DBMS is more reliable
and reactive
Centralized DBMS is less
sophisticated
Distributed DBMS is more
sophisticated
Presence of a global clock Lack of a global clock
One single central unit Concurrency of components
2. Explain various factors that affect the selection of DBMS software.
Ans Data Model For a long time, the relational concept was dominant,
however recently NoSQL databases have again become more successful.
Relational vs. NoSQL is based on your individual needs and their main
advantage is the data structure itself.
To decide on which model works best for you, you should ask yourself: Do you
have a data structure which you can easily reflect in a relational model or do
you need to work with unstructured data? How do you retrieve and work with
the data? For example, analysis of hierarchical data in sequential files is faster
in a NoSQL database then in a relational one. Since relational databases have a
long history, you find a lot of commercial RDBMS (relational DBMS), whereas
NoSQL databases are often available as open source.
2. Data Consistency
Nowadays, collecting data is not a big effort any more. But, keeping the data
consistent becomes even more important as more sources feed into the
database. Therefore, consistency rules are very important and the ability to
define these should be considered when choosing a new DBMS.
3. Data Security For most companies, data availability is a key business success
factor and should be guaranteed at all times. The ability to backup and restore
the databases is essential and needs to be possible with your chosen DBMS. IT
Administrators should setup a framework and a management plan for data
security and ensure as little downtime as possible.
4. Data Protection Access protection and encryption should allow protection
of personal data. Every DBMS provide different methods of protect the data
through encryption, but the possibility to define routines and access rights is
different for every system. The method of data protection depends on the
structure of data and should be carefully considered during the evaluation
process of a DBMS.
5. Multi Access and Integration Setting up a DBMS, running it and extending it
for future growth, requires enough flexibility to allow integration into the given
IT infrastructure. Furthermore, the DBMS needs to allow concurrent accesses
by multiple users. Synchronization and integration with other tools are
essential for smooth workflows.
6. Efficiency When we talk about the efficiency of DBMS, we usually mean the
response time. You will find on premise and cloud solutions available on the
market. Depending on your own IT infrastructure, a cloud based solution can
have certain disadvantages, as you rely on network services and latencies of
network providers. On the other side, cloud computing can provide more and
better resources compared to your on premise infrastructure, as efficiency is
also related to scalability. Make sure your DBMS of choice can scale to your
needs.
7. Usability Different user groups will be working with the DBMS. There are the
administrators, IT and Database admins, application integrators and data
consumers. All these different roles need an easily understandable query
language and intuitive UI to use the DBMS system efficiently. The easier it is for
the user to work with the DBMS, the lower the cost will be for people.
8. Implementation and Service Costs The modifiability and availability of
support and documentation needs to be taken into consideration as part of the
implementation and Total Cost of Ownership (TCO). Development needs must
always be included, as Database Management Systems need to be shaped to
the individual company’s need. A clear overview of these needs and costs will
help to choose the right tool. Vendor or community support as well as
comprehensive documentation will save you time and money.
3. Check if a given a year is a leap year. The condition is:-
Year should be (divisible by 4 and not divisible by 100) or (divisible by 4 and
divisible by 400.) Display the output on the screen using
dbms_output.put_line. The year should be input by the user.
Ans DECLARE
year NUMBER: = 1600;
BEGIN
IF MOD (year, 4) =0
AND
MOD (year, 100)!=0
OR
MOD (year, 400) =0 THEN
dbms_output.Put_line(year
|| ' is a leap year ');
ELSE
dbms_output.Put_line(year
|| ' is not a leap year.');
END IF;
END;
4. Ask the user to enter the weight of an apple box. If the
weight is >= 10 kg, rate =Rs. 5/kg
weight is < 10 kg, rate = Rs. 7/kg
Calculate the cost of the apple box. Display the output on the screen using
dbms_output.put_line
Ans DECLARE
weight number:=&apple_weight;
rate number;
BEGIN
IF (weight>=10) THEN
rate:=5;
ELSE
rate:=7;
END IF;
dbms_output.put_line('TOTAL WEIGHT'||weight||'TOTAL COST
'||(weight*rate));
END;
/
5. Program should accept the age of the user. Depending upon the following
conditions it should output:-
age <18 years? Child?
Age >= 18 years and <21 years? Major?
Age >= 21years? Adult?
Display the output on the screen using dbms_output.put_line
Ans DECLARE
age number:=&user_age;
USER varchar(20);
BEGIN
IF (age<18) THEN
USER:='child';
elsif (age>18) AND (age<21) THEN
USER:='major';
ELSE
USER:='adult';
END IF;
dbms_output.put_line('user is '||USER);
END;
/

More Related Content

Similar to ADBMS 19MCA8125.pdf

DBMS-1.pptx
DBMS-1.pptxDBMS-1.pptx
DBMS-1.pptx
kingVox
 
Big Data using NoSQL Technologies
Big Data using NoSQL TechnologiesBig Data using NoSQL Technologies
Big Data using NoSQL Technologies
Amit Singh
 
Hybrid & Multi-cloud Environment.pdf
Hybrid & Multi-cloud Environment.pdfHybrid & Multi-cloud Environment.pdf
Hybrid & Multi-cloud Environment.pdf
manoharparakh
 
IPM Individual Assignment.docx
IPM Individual Assignment.docxIPM Individual Assignment.docx
IPM Individual Assignment.docx
Mikealay Desta
 
A blueprint for data in a multicloud world
A blueprint for data in a multicloud worldA blueprint for data in a multicloud world
A blueprint for data in a multicloud world
Mehdi Charafeddine
 
Intro to big data and applications -day 3
Intro to big data and applications -day 3Intro to big data and applications -day 3
Intro to big data and applications -day 3
Parviz Vakili
 
Itc571 Project Presentation
Itc571 Project PresentationItc571 Project Presentation
Itc571 Project Presentation
Dinh Khue
 
My seminar on distributed dbms
My seminar on distributed dbmsMy seminar on distributed dbms
My seminar on distributed dbms
Vinay D. Patel
 
data base management report
data base management report data base management report
data base management report
shivam tripathi
 
GigaOm-sector-roadmap-cloud-analytic-databases-2017
GigaOm-sector-roadmap-cloud-analytic-databases-2017GigaOm-sector-roadmap-cloud-analytic-databases-2017
GigaOm-sector-roadmap-cloud-analytic-databases-2017
Jeremy Maranitch
 
Mi0034 database management systems
Mi0034  database management systemsMi0034  database management systems
Mi0034 database management systems
smumbahelp
 
Microsoft SQL Azure - Scaling Out with SQL Azure Whitepaper
Microsoft SQL Azure - Scaling Out with SQL Azure WhitepaperMicrosoft SQL Azure - Scaling Out with SQL Azure Whitepaper
Microsoft SQL Azure - Scaling Out with SQL Azure Whitepaper
Microsoft Private Cloud
 
AtomicDBCoreTech_White Papaer
AtomicDBCoreTech_White PapaerAtomicDBCoreTech_White Papaer
AtomicDBCoreTech_White Papaer
JEAN-MICHEL LETENNIER
 
Unit 1: Introduction to DBMS Unit 1 Complete
Unit 1: Introduction to DBMS Unit 1 CompleteUnit 1: Introduction to DBMS Unit 1 Complete
Unit 1: Introduction to DBMS Unit 1 Complete
Raj vardhan
 
database disegn.pptx
database disegn.pptxdatabase disegn.pptx
database disegn.pptx
BARHAMMUSIC
 
data-mesh_whitepaper_dec2021.pdf
data-mesh_whitepaper_dec2021.pdfdata-mesh_whitepaper_dec2021.pdf
data-mesh_whitepaper_dec2021.pdf
ssuser18927d
 
Case study 9
Case study 9Case study 9
Case study 9
khaled alsaeh
 
Dynamo Amazon’s Highly Available Key-value Store Giuseppe D.docx
Dynamo Amazon’s Highly Available Key-value Store Giuseppe D.docxDynamo Amazon’s Highly Available Key-value Store Giuseppe D.docx
Dynamo Amazon’s Highly Available Key-value Store Giuseppe D.docx
jacksnathalie
 
Basic and Introduction to DBMS Unit 1 of AU
Basic and Introduction to DBMS Unit 1 of AUBasic and Introduction to DBMS Unit 1 of AU
Basic and Introduction to DBMS Unit 1 of AU
infant2404
 
LESSON 1 - DATABASE MANAGEMENT SYSTEM.pptx
LESSON 1 - DATABASE MANAGEMENT SYSTEM.pptxLESSON 1 - DATABASE MANAGEMENT SYSTEM.pptx
LESSON 1 - DATABASE MANAGEMENT SYSTEM.pptx
calf_ville86
 

Similar to ADBMS 19MCA8125.pdf (20)

DBMS-1.pptx
DBMS-1.pptxDBMS-1.pptx
DBMS-1.pptx
 
Big Data using NoSQL Technologies
Big Data using NoSQL TechnologiesBig Data using NoSQL Technologies
Big Data using NoSQL Technologies
 
Hybrid & Multi-cloud Environment.pdf
Hybrid & Multi-cloud Environment.pdfHybrid & Multi-cloud Environment.pdf
Hybrid & Multi-cloud Environment.pdf
 
IPM Individual Assignment.docx
IPM Individual Assignment.docxIPM Individual Assignment.docx
IPM Individual Assignment.docx
 
A blueprint for data in a multicloud world
A blueprint for data in a multicloud worldA blueprint for data in a multicloud world
A blueprint for data in a multicloud world
 
Intro to big data and applications -day 3
Intro to big data and applications -day 3Intro to big data and applications -day 3
Intro to big data and applications -day 3
 
Itc571 Project Presentation
Itc571 Project PresentationItc571 Project Presentation
Itc571 Project Presentation
 
My seminar on distributed dbms
My seminar on distributed dbmsMy seminar on distributed dbms
My seminar on distributed dbms
 
data base management report
data base management report data base management report
data base management report
 
GigaOm-sector-roadmap-cloud-analytic-databases-2017
GigaOm-sector-roadmap-cloud-analytic-databases-2017GigaOm-sector-roadmap-cloud-analytic-databases-2017
GigaOm-sector-roadmap-cloud-analytic-databases-2017
 
Mi0034 database management systems
Mi0034  database management systemsMi0034  database management systems
Mi0034 database management systems
 
Microsoft SQL Azure - Scaling Out with SQL Azure Whitepaper
Microsoft SQL Azure - Scaling Out with SQL Azure WhitepaperMicrosoft SQL Azure - Scaling Out with SQL Azure Whitepaper
Microsoft SQL Azure - Scaling Out with SQL Azure Whitepaper
 
AtomicDBCoreTech_White Papaer
AtomicDBCoreTech_White PapaerAtomicDBCoreTech_White Papaer
AtomicDBCoreTech_White Papaer
 
Unit 1: Introduction to DBMS Unit 1 Complete
Unit 1: Introduction to DBMS Unit 1 CompleteUnit 1: Introduction to DBMS Unit 1 Complete
Unit 1: Introduction to DBMS Unit 1 Complete
 
database disegn.pptx
database disegn.pptxdatabase disegn.pptx
database disegn.pptx
 
data-mesh_whitepaper_dec2021.pdf
data-mesh_whitepaper_dec2021.pdfdata-mesh_whitepaper_dec2021.pdf
data-mesh_whitepaper_dec2021.pdf
 
Case study 9
Case study 9Case study 9
Case study 9
 
Dynamo Amazon’s Highly Available Key-value Store Giuseppe D.docx
Dynamo Amazon’s Highly Available Key-value Store Giuseppe D.docxDynamo Amazon’s Highly Available Key-value Store Giuseppe D.docx
Dynamo Amazon’s Highly Available Key-value Store Giuseppe D.docx
 
Basic and Introduction to DBMS Unit 1 of AU
Basic and Introduction to DBMS Unit 1 of AUBasic and Introduction to DBMS Unit 1 of AU
Basic and Introduction to DBMS Unit 1 of AU
 
LESSON 1 - DATABASE MANAGEMENT SYSTEM.pptx
LESSON 1 - DATABASE MANAGEMENT SYSTEM.pptxLESSON 1 - DATABASE MANAGEMENT SYSTEM.pptx
LESSON 1 - DATABASE MANAGEMENT SYSTEM.pptx
 

More from 19BAG7124SAHIL

Lecture 1.ppt
Lecture 1.pptLecture 1.ppt
Lecture 1.ppt
19BAG7124SAHIL
 
Farm management production and resource econmics (1).pdf
Farm management production and resource econmics (1).pdfFarm management production and resource econmics (1).pdf
Farm management production and resource econmics (1).pdf
19BAG7124SAHIL
 
Geranium and Vetiver.pptx
Geranium and Vetiver.pptxGeranium and Vetiver.pptx
Geranium and Vetiver.pptx
19BAG7124SAHIL
 
COTTON.pptx
COTTON.pptxCOTTON.pptx
COTTON.pptx
19BAG7124SAHIL
 
Econ_Unit-1 (1).pptx
Econ_Unit-1 (1).pptxEcon_Unit-1 (1).pptx
Econ_Unit-1 (1).pptx
19BAG7124SAHIL
 
EXPERIMENT 4 stock solution nutrient medium.pptx
EXPERIMENT 4 stock solution nutrient medium.pptxEXPERIMENT 4 stock solution nutrient medium.pptx
EXPERIMENT 4 stock solution nutrient medium.pptx
19BAG7124SAHIL
 
COW PEA.pptx
COW PEA.pptxCOW PEA.pptx
COW PEA.pptx
19BAG7124SAHIL
 
Capacity building o Extension personnel.pptx
Capacity building o Extension personnel.pptxCapacity building o Extension personnel.pptx
Capacity building o Extension personnel.pptx
19BAG7124SAHIL
 
centres of origin-biodiversity and its significance.pptx
centres of origin-biodiversity and its significance.pptxcentres of origin-biodiversity and its significance.pptx
centres of origin-biodiversity and its significance.pptx
19BAG7124SAHIL
 
Rural Development.pptx
Rural Development.pptxRural Development.pptx
Rural Development.pptx
19BAG7124SAHIL
 

More from 19BAG7124SAHIL (10)

Lecture 1.ppt
Lecture 1.pptLecture 1.ppt
Lecture 1.ppt
 
Farm management production and resource econmics (1).pdf
Farm management production and resource econmics (1).pdfFarm management production and resource econmics (1).pdf
Farm management production and resource econmics (1).pdf
 
Geranium and Vetiver.pptx
Geranium and Vetiver.pptxGeranium and Vetiver.pptx
Geranium and Vetiver.pptx
 
COTTON.pptx
COTTON.pptxCOTTON.pptx
COTTON.pptx
 
Econ_Unit-1 (1).pptx
Econ_Unit-1 (1).pptxEcon_Unit-1 (1).pptx
Econ_Unit-1 (1).pptx
 
EXPERIMENT 4 stock solution nutrient medium.pptx
EXPERIMENT 4 stock solution nutrient medium.pptxEXPERIMENT 4 stock solution nutrient medium.pptx
EXPERIMENT 4 stock solution nutrient medium.pptx
 
COW PEA.pptx
COW PEA.pptxCOW PEA.pptx
COW PEA.pptx
 
Capacity building o Extension personnel.pptx
Capacity building o Extension personnel.pptxCapacity building o Extension personnel.pptx
Capacity building o Extension personnel.pptx
 
centres of origin-biodiversity and its significance.pptx
centres of origin-biodiversity and its significance.pptxcentres of origin-biodiversity and its significance.pptx
centres of origin-biodiversity and its significance.pptx
 
Rural Development.pptx
Rural Development.pptxRural Development.pptx
Rural Development.pptx
 

Recently uploaded

Applied Science: Thermodynamics, Laws & Methodology.pdf
Applied Science: Thermodynamics, Laws & Methodology.pdfApplied Science: Thermodynamics, Laws & Methodology.pdf
Applied Science: Thermodynamics, Laws & Methodology.pdf
University of Hertfordshire
 
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
ABHISHEK SONI NIMT INSTITUTE OF MEDICAL AND PARAMEDCIAL SCIENCES , GOVT PG COLLEGE NOIDA
 
Microbiology of Central Nervous System INFECTIONS.pdf
Microbiology of Central Nervous System INFECTIONS.pdfMicrobiology of Central Nervous System INFECTIONS.pdf
Microbiology of Central Nervous System INFECTIONS.pdf
sammy700571
 
Alternate Wetting and Drying - Climate Smart Agriculture
Alternate Wetting and Drying - Climate Smart AgricultureAlternate Wetting and Drying - Climate Smart Agriculture
Alternate Wetting and Drying - Climate Smart Agriculture
International Food Policy Research Institute- South Asia Office
 
Direct Seeded Rice - Climate Smart Agriculture
Direct Seeded Rice - Climate Smart AgricultureDirect Seeded Rice - Climate Smart Agriculture
Direct Seeded Rice - Climate Smart Agriculture
International Food Policy Research Institute- South Asia Office
 
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...
Sérgio Sacani
 
The debris of the ‘last major merger’ is dynamically young
The debris of the ‘last major merger’ is dynamically youngThe debris of the ‘last major merger’ is dynamically young
The debris of the ‘last major merger’ is dynamically young
Sérgio Sacani
 
Summary Of transcription and Translation.pdf
Summary Of transcription and Translation.pdfSummary Of transcription and Translation.pdf
Summary Of transcription and Translation.pdf
vadgavevedant86
 
LEARNING TO LIVE WITH LAWS OF MOTION .pptx
LEARNING TO LIVE WITH LAWS OF MOTION .pptxLEARNING TO LIVE WITH LAWS OF MOTION .pptx
LEARNING TO LIVE WITH LAWS OF MOTION .pptx
yourprojectpartner05
 
Tissue fluids_etiology_volume regulation_pressure.pptx
Tissue fluids_etiology_volume regulation_pressure.pptxTissue fluids_etiology_volume regulation_pressure.pptx
Tissue fluids_etiology_volume regulation_pressure.pptx
muralinath2
 
快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样
快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样
快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样
hozt8xgk
 
fermented food science of sauerkraut.pptx
fermented food science of sauerkraut.pptxfermented food science of sauerkraut.pptx
fermented food science of sauerkraut.pptx
ananya23nair
 
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Leonel Morgado
 
CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)
CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)
CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)
eitps1506
 
11.1 Role of physical biological in deterioration of grains.pdf
11.1 Role of physical biological in deterioration of grains.pdf11.1 Role of physical biological in deterioration of grains.pdf
11.1 Role of physical biological in deterioration of grains.pdf
PirithiRaju
 
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at  𝐳 = 2.9  wi...Discovery of An Apparent Red, High-Velocity Type Ia Supernova at  𝐳 = 2.9  wi...
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...
Sérgio Sacani
 
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Sérgio Sacani
 
Immersive Learning That Works: Research Grounding and Paths Forward
Immersive Learning That Works: Research Grounding and Paths ForwardImmersive Learning That Works: Research Grounding and Paths Forward
Immersive Learning That Works: Research Grounding and Paths Forward
Leonel Morgado
 
Randomised Optimisation Algorithms in DAPHNE
Randomised Optimisation Algorithms in DAPHNERandomised Optimisation Algorithms in DAPHNE
Randomised Optimisation Algorithms in DAPHNE
University of Maribor
 
The binding of cosmological structures by massless topological defects
The binding of cosmological structures by massless topological defectsThe binding of cosmological structures by massless topological defects
The binding of cosmological structures by massless topological defects
Sérgio Sacani
 

Recently uploaded (20)

Applied Science: Thermodynamics, Laws & Methodology.pdf
Applied Science: Thermodynamics, Laws & Methodology.pdfApplied Science: Thermodynamics, Laws & Methodology.pdf
Applied Science: Thermodynamics, Laws & Methodology.pdf
 
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
 
Microbiology of Central Nervous System INFECTIONS.pdf
Microbiology of Central Nervous System INFECTIONS.pdfMicrobiology of Central Nervous System INFECTIONS.pdf
Microbiology of Central Nervous System INFECTIONS.pdf
 
Alternate Wetting and Drying - Climate Smart Agriculture
Alternate Wetting and Drying - Climate Smart AgricultureAlternate Wetting and Drying - Climate Smart Agriculture
Alternate Wetting and Drying - Climate Smart Agriculture
 
Direct Seeded Rice - Climate Smart Agriculture
Direct Seeded Rice - Climate Smart AgricultureDirect Seeded Rice - Climate Smart Agriculture
Direct Seeded Rice - Climate Smart Agriculture
 
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...
 
The debris of the ‘last major merger’ is dynamically young
The debris of the ‘last major merger’ is dynamically youngThe debris of the ‘last major merger’ is dynamically young
The debris of the ‘last major merger’ is dynamically young
 
Summary Of transcription and Translation.pdf
Summary Of transcription and Translation.pdfSummary Of transcription and Translation.pdf
Summary Of transcription and Translation.pdf
 
LEARNING TO LIVE WITH LAWS OF MOTION .pptx
LEARNING TO LIVE WITH LAWS OF MOTION .pptxLEARNING TO LIVE WITH LAWS OF MOTION .pptx
LEARNING TO LIVE WITH LAWS OF MOTION .pptx
 
Tissue fluids_etiology_volume regulation_pressure.pptx
Tissue fluids_etiology_volume regulation_pressure.pptxTissue fluids_etiology_volume regulation_pressure.pptx
Tissue fluids_etiology_volume regulation_pressure.pptx
 
快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样
快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样
快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样
 
fermented food science of sauerkraut.pptx
fermented food science of sauerkraut.pptxfermented food science of sauerkraut.pptx
fermented food science of sauerkraut.pptx
 
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
 
CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)
CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)
CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)
 
11.1 Role of physical biological in deterioration of grains.pdf
11.1 Role of physical biological in deterioration of grains.pdf11.1 Role of physical biological in deterioration of grains.pdf
11.1 Role of physical biological in deterioration of grains.pdf
 
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at  𝐳 = 2.9  wi...Discovery of An Apparent Red, High-Velocity Type Ia Supernova at  𝐳 = 2.9  wi...
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...
 
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
 
Immersive Learning That Works: Research Grounding and Paths Forward
Immersive Learning That Works: Research Grounding and Paths ForwardImmersive Learning That Works: Research Grounding and Paths Forward
Immersive Learning That Works: Research Grounding and Paths Forward
 
Randomised Optimisation Algorithms in DAPHNE
Randomised Optimisation Algorithms in DAPHNERandomised Optimisation Algorithms in DAPHNE
Randomised Optimisation Algorithms in DAPHNE
 
The binding of cosmological structures by massless topological defects
The binding of cosmological structures by massless topological defectsThe binding of cosmological structures by massless topological defects
The binding of cosmological structures by massless topological defects
 

ADBMS 19MCA8125.pdf

  • 1. Name - Shubham Pathania Class - MCA (LEET) UID - 19MCA8125 Subject - ADBMS (CAT-762) Group - 4th Submitted to Submitted by Sandeep Kaur Shubham Pathania
  • 2. INDEX Sr No. Contents Page no. 1 Compare and Contrast multi database system with centralized system 1-2 2 Explain various factors that affect the selection of DBMS software. 2-3 3 Check if a given a year is a leap year. The condition is:- Year should be (divisible by 4 and not divisible by 100) or (divisible by 4 and divisible by 400.) Display the output on the screen using dbms_output.put_line. The year should be input by the user. 4 4 Ask the user to enter the weight of an apple box. If the weight is >= 10 kg, rate =Rs. 5/kg weight is < 10 kg, rate = Rs. 7/kg Calculate the cost of the apple box. Display the output on the screen using dbms_output.put_line 5 5 Program should accept the age of the user. Depending upon the following conditions it should output:- age <18 years? Child? Age >= 18 years and <21 years? Major? Age >= 21years? Adult? 6
  • 3. 1. Compare and Contrast multi database system with centralized system? Ans There are many aspect that let us make a comparison between centralized and distributed DBMS:  Database management system is any software that manages and controls the storage, the organization, security, retrieval and integral of data in a specific database, whereas DDBMS consist of a single database that is divided into many fragments. Each fragment is integrated on one or more computer and controlled by independent database (DBMS).  In centralized DBMS the data is distributed across the network computers, and the data is stored on many sites and under the management responsibility of DDBMS. But in the DBMS data is stored and controlled in a central site.  Both of DDBMS and centralized DBMS provide the access to database using the same interface, but for this function centralized DBMS faces less complication than DDBMS.  For distributing data over network we can use replication or fragmentation. The objective of replication and fragmentation is to make a transparency of this allocation to make the details of implementation hidden on users. In centralized DBMS is not need to make transparencies.  In DDBMS design we can find three issues which are not in centralized DBMS design.  Consequently, centralized DBMS is less sophisticated than DDBMS because it not supports the organizational structure of today’s widely distributed enterprises, and DDBMS more reactive and reliable. Difference - Centralized DBMS Distributed DBMS Data is stored only on one site Data is stored on different sites Data stored in single computer can be used by multiple users Data is stored over different sites which are connected with each other. If centralized system fails, then the entire system is halted. If one of the system fails, then user can access the data from other sites.
  • 4. Centralized DBMS Distributed DBMS Centralized DBMS is less reliable and reactive Distributed DBMS is more reliable and reactive Centralized DBMS is less sophisticated Distributed DBMS is more sophisticated Presence of a global clock Lack of a global clock One single central unit Concurrency of components 2. Explain various factors that affect the selection of DBMS software. Ans Data Model For a long time, the relational concept was dominant, however recently NoSQL databases have again become more successful. Relational vs. NoSQL is based on your individual needs and their main advantage is the data structure itself. To decide on which model works best for you, you should ask yourself: Do you have a data structure which you can easily reflect in a relational model or do you need to work with unstructured data? How do you retrieve and work with the data? For example, analysis of hierarchical data in sequential files is faster in a NoSQL database then in a relational one. Since relational databases have a long history, you find a lot of commercial RDBMS (relational DBMS), whereas NoSQL databases are often available as open source. 2. Data Consistency Nowadays, collecting data is not a big effort any more. But, keeping the data consistent becomes even more important as more sources feed into the database. Therefore, consistency rules are very important and the ability to define these should be considered when choosing a new DBMS. 3. Data Security For most companies, data availability is a key business success factor and should be guaranteed at all times. The ability to backup and restore the databases is essential and needs to be possible with your chosen DBMS. IT Administrators should setup a framework and a management plan for data security and ensure as little downtime as possible. 4. Data Protection Access protection and encryption should allow protection of personal data. Every DBMS provide different methods of protect the data through encryption, but the possibility to define routines and access rights is different for every system. The method of data protection depends on the
  • 5. structure of data and should be carefully considered during the evaluation process of a DBMS. 5. Multi Access and Integration Setting up a DBMS, running it and extending it for future growth, requires enough flexibility to allow integration into the given IT infrastructure. Furthermore, the DBMS needs to allow concurrent accesses by multiple users. Synchronization and integration with other tools are essential for smooth workflows. 6. Efficiency When we talk about the efficiency of DBMS, we usually mean the response time. You will find on premise and cloud solutions available on the market. Depending on your own IT infrastructure, a cloud based solution can have certain disadvantages, as you rely on network services and latencies of network providers. On the other side, cloud computing can provide more and better resources compared to your on premise infrastructure, as efficiency is also related to scalability. Make sure your DBMS of choice can scale to your needs. 7. Usability Different user groups will be working with the DBMS. There are the administrators, IT and Database admins, application integrators and data consumers. All these different roles need an easily understandable query language and intuitive UI to use the DBMS system efficiently. The easier it is for the user to work with the DBMS, the lower the cost will be for people. 8. Implementation and Service Costs The modifiability and availability of support and documentation needs to be taken into consideration as part of the implementation and Total Cost of Ownership (TCO). Development needs must always be included, as Database Management Systems need to be shaped to the individual company’s need. A clear overview of these needs and costs will help to choose the right tool. Vendor or community support as well as comprehensive documentation will save you time and money. 3. Check if a given a year is a leap year. The condition is:- Year should be (divisible by 4 and not divisible by 100) or (divisible by 4 and divisible by 400.) Display the output on the screen using dbms_output.put_line. The year should be input by the user. Ans DECLARE year NUMBER: = 1600; BEGIN IF MOD (year, 4) =0
  • 6. AND MOD (year, 100)!=0 OR MOD (year, 400) =0 THEN dbms_output.Put_line(year || ' is a leap year '); ELSE dbms_output.Put_line(year || ' is not a leap year.'); END IF; END; 4. Ask the user to enter the weight of an apple box. If the weight is >= 10 kg, rate =Rs. 5/kg weight is < 10 kg, rate = Rs. 7/kg Calculate the cost of the apple box. Display the output on the screen using dbms_output.put_line Ans DECLARE weight number:=&apple_weight; rate number; BEGIN IF (weight>=10) THEN rate:=5; ELSE rate:=7; END IF; dbms_output.put_line('TOTAL WEIGHT'||weight||'TOTAL COST '||(weight*rate)); END; /
  • 7. 5. Program should accept the age of the user. Depending upon the following conditions it should output:- age <18 years? Child? Age >= 18 years and <21 years? Major? Age >= 21years? Adult? Display the output on the screen using dbms_output.put_line Ans DECLARE age number:=&user_age; USER varchar(20); BEGIN IF (age<18) THEN USER:='child'; elsif (age>18) AND (age<21) THEN USER:='major'; ELSE USER:='adult'; END IF; dbms_output.put_line('user is '||USER); END; /