This document provides an introduction and overview of a course on cloud computing. The course objectives are to understand concepts and infrastructure of cloud computing, opportunities and challenges of information management in complex environments, current techniques and tools for cloud applications, and ethical, legal and social issues related to cloud computing. The syllabus is divided into 5 modules that cover introduction to cloud computing, using cloud computing for various uses, governance in the cloud, working with cloud services, and external cloud storage and sharing. Assessment includes exams, assignments, and self-work/professional skills development activities.
A Generic Model for Student Data Analytic Web Service (SDAWS)Editor IJCATR
Â
Any university management system accumulates a cartload of data and analytics can be applied on it to gather useful
information to aid the academic decision making process. This paper is a novel attempt to demonstrate the significance of a data
analytic web service in the education domain. This can be integrated with the University Management System or any other application
of the university easily. Analytics as a web service offers much benefits over the traditional analysis methods. The web service can be
hosted on a web server and accessed over the internet or on to the private cloud of the campus. The data from various courses from
different departments can be uploaded and analyzed easily. In this paper we design a web service framework to be used in educational
data mining that provide analysis as a service.
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find more Data-Ed webinars here: www.datablueprint.com
Data-Ed Online: Data Architecture RequirementsDATAVERSITY
Â
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
A Generic Model for Student Data Analytic Web Service (SDAWS)Editor IJCATR
Â
Any university management system accumulates a cartload of data and analytics can be applied on it to gather useful
information to aid the academic decision making process. This paper is a novel attempt to demonstrate the significance of a data
analytic web service in the education domain. This can be integrated with the University Management System or any other application
of the university easily. Analytics as a web service offers much benefits over the traditional analysis methods. The web service can be
hosted on a web server and accessed over the internet or on to the private cloud of the campus. The data from various courses from
different departments can be uploaded and analyzed easily. In this paper we design a web service framework to be used in educational
data mining that provide analysis as a service.
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find more Data-Ed webinars here: www.datablueprint.com
Data-Ed Online: Data Architecture RequirementsDATAVERSITY
Â
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
Challenges of Learning Management Systems and Current TrendsIJAEMSJORNAL
Â
Information and communication technologies (ICT) and Learning Management Systems (LMSs) are important multifunctional tools developed for higher education institutions, but in fact, the success of these systems largely depends on a detailed understanding of the challenges and factors that influence the e-learning of their users. During the quarantine period due to Covid-19 in the world, Learning Management Systems were used worldwide in Higher Education as software or method to promote the course delivery. Since it was the first experience of many Egyptian higher education institutions with the use of the learning management systems during the pandemic, challenges were expected. This research aims to examine the challenges faced by LMS use and the factors influencing its use among teachers and students. The results of this research could help researchers, policy-makers, and practitioners from public and private universities to gather insights on the successful application and use of LMS during and after Covid19.
Webinar CRUI Dell: flexilab, computer classroom made flexible JĂźrgen Ambrosi
Â
Nella cornice dei seminari CRUI, Dell presenta la piattaforma Flexilab, una soluzione innovativa per le computer classroom, che consente allo studente di accedere alle applicazioni di cui necessita da qualunque dispositivo e da qualunque luogo e alle universitĂ di dotarsi delle piĂš recenti tecnologie, in una modalitĂ gestita, flessibile ed economicamente vantaggiosa.
Now Refer your #College or #University to #EMC Academic Alliance, a collaboration with colleges and universities worldwide to help prepare #students for successful #careers in a transforming #ITindustry via http://bit.ly/RefertoEMCAA
MLOps â Applying DevOps to Competitive AdvantageDATAVERSITY
Â
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of âmachine learningâ and âoperations,â MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
5 Key Data Management Trends of 2022 as observed by a data practitioner. Covers trends on data architecture, data storage, data platforms, and data operations.
Want to know more about Common Data Model and Service? You need to understant what's the difference between CDS for Apps and Analytics? Feel free to use these slides and send me your feed backs.
Data Systems Integration & Business Value Pt. 2: CloudDATAVERSITY
Â
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Data Systems Integration & Business Value Pt. 2: CloudData Blueprint
Â
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Many organizations are modifying their IT portfolios to fully take advantage of the benefits of cloud computing. While the motivation is specific and focuses on broad-based challenges, all organizations are prepared to benefit from aspects of the cloud. This is accomplished by ensuring that cloud-hosted data share three attributes. Cloud-hosted datasets must be of:
Higher quality data than those data residing outside of the cloud;
Lower volume (1/5 the size of data collections) than similar collections residing outside of the cloud; and
Increased share-ability than data residing outside the cloud.
Increases in capacity utilization, improved IT flexibility and responsiveness, as well as the forecast decreases in cost accruing to cloud-based computing are all possible after these first three conditions have been met. Necessary investments in data engineering can help organizations to save even more money by reducing the amount of resources required to perform their duties and increasing the effectiveness of their duties and decision-making. This webinar will show you how to recognize the opportunities, âsize upâ the required investment, and properly supervise your efforts to take advantage of the opportunities presented by the cloud.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bankâs data infrastructure but also positioned them as pioneers in the banking sectorâs adoption of graph technology.
Challenges of Learning Management Systems and Current TrendsIJAEMSJORNAL
Â
Information and communication technologies (ICT) and Learning Management Systems (LMSs) are important multifunctional tools developed for higher education institutions, but in fact, the success of these systems largely depends on a detailed understanding of the challenges and factors that influence the e-learning of their users. During the quarantine period due to Covid-19 in the world, Learning Management Systems were used worldwide in Higher Education as software or method to promote the course delivery. Since it was the first experience of many Egyptian higher education institutions with the use of the learning management systems during the pandemic, challenges were expected. This research aims to examine the challenges faced by LMS use and the factors influencing its use among teachers and students. The results of this research could help researchers, policy-makers, and practitioners from public and private universities to gather insights on the successful application and use of LMS during and after Covid19.
Webinar CRUI Dell: flexilab, computer classroom made flexible JĂźrgen Ambrosi
Â
Nella cornice dei seminari CRUI, Dell presenta la piattaforma Flexilab, una soluzione innovativa per le computer classroom, che consente allo studente di accedere alle applicazioni di cui necessita da qualunque dispositivo e da qualunque luogo e alle universitĂ di dotarsi delle piĂš recenti tecnologie, in una modalitĂ gestita, flessibile ed economicamente vantaggiosa.
Now Refer your #College or #University to #EMC Academic Alliance, a collaboration with colleges and universities worldwide to help prepare #students for successful #careers in a transforming #ITindustry via http://bit.ly/RefertoEMCAA
MLOps â Applying DevOps to Competitive AdvantageDATAVERSITY
Â
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of âmachine learningâ and âoperations,â MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
5 Key Data Management Trends of 2022 as observed by a data practitioner. Covers trends on data architecture, data storage, data platforms, and data operations.
Want to know more about Common Data Model and Service? You need to understant what's the difference between CDS for Apps and Analytics? Feel free to use these slides and send me your feed backs.
Data Systems Integration & Business Value Pt. 2: CloudDATAVERSITY
Â
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Data Systems Integration & Business Value Pt. 2: CloudData Blueprint
Â
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Many organizations are modifying their IT portfolios to fully take advantage of the benefits of cloud computing. While the motivation is specific and focuses on broad-based challenges, all organizations are prepared to benefit from aspects of the cloud. This is accomplished by ensuring that cloud-hosted data share three attributes. Cloud-hosted datasets must be of:
Higher quality data than those data residing outside of the cloud;
Lower volume (1/5 the size of data collections) than similar collections residing outside of the cloud; and
Increased share-ability than data residing outside the cloud.
Increases in capacity utilization, improved IT flexibility and responsiveness, as well as the forecast decreases in cost accruing to cloud-based computing are all possible after these first three conditions have been met. Necessary investments in data engineering can help organizations to save even more money by reducing the amount of resources required to perform their duties and increasing the effectiveness of their duties and decision-making. This webinar will show you how to recognize the opportunities, âsize upâ the required investment, and properly supervise your efforts to take advantage of the opportunities presented by the cloud.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
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Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bankâs data infrastructure but also positioned them as pioneers in the banking sectorâs adoption of graph technology.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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L1-Introduction to Cloud Computing.ppt
1. Amity Institute of Information Technology
Introduction to Cloud Computing
By
Amrit Thulal
Amity Institute of Information Technology
Amity University,
Uttar Pradesh
2. Amity Institute of Information Technology
Digital Marketing Overview
Course Objectives
Course Objectives: After finishing this course student will be
able to:
⢠Concepts and infrastructure of cloud computing and its
business applications.
⢠Critically appraise the opportunities and challenges of
information management in complex business environment.
⢠To use current techniques, skills, and tools necessary for Cloud
Applications.
⢠To understand the role and responsibilities of professional field,
how to deal with ethical, legal, security and social issues and
responsibilities related to cloud computing.
⢠To gain expertise in implementation and development of cloud
enabled businessapplication
2
3. Amity Institute of Information Technology
Digital Marketing Overview
Syllabus
3
Weightage (%)
Module I: Introduction 20
Defining the cloud for the Enterprise: Database as a service,
Governance/Management as a service, Testing as a service, Storage
as a service , Cloud service development, Cloud Computing
Challenges Layersof Cloud Computing, types of cloud computing,
Cloud Computing Features, Cloud Computing Security requirements,
pros and cons, benefits
Module II:Cloud Computing For Everyone 25
Centralizing email communications, cloud computing for community
Collaborating on Schedules, Collaborating on GroceryLists,
Collaborating on ToDoLists Collaborating on ContactLists,
Collaboratingon schedules, collaborating on group projects and
events, cloud computing for corporation, mapping, schedules
managing projects, Collaborating on Marketing Materials,
Collaboratingon Expense Reports, Collaborating on Budgets,
Collaborating on Financial Statements, Presenting on the Road,
Accessing Documents on the Road.
4. Amity Institute of Information Technology
Digital Marketing Overview
Syllabus
4
Weightage (%)
Module III: Brining Governance to the clouds 15
People and processes, Governance for the clouds, Creating the Governance
model: Define Polices, design Polices, Implement policies, Governance
technology.
Module IV:Working from your Services to Clouds andCloud Services 25
Descriptors/Topics Descriptors/Topics
Defining MetaServices and Service, Creating the service directory,
Collaborating on calendars, Schedules and task management, exploring on
line scheduling and planning, collaborating on event management,
collaborating on contact management, collaborating on project management,
collaborating on word processing, spreadsheets, and databases.
Module V:Outside Cloud Storing and Sharing 15
Evaluating on line file storage ,Evaluating web conference tools ,Evaluating
web mail services, Evaluating instant messaging, creating groups onsocial
networks, Evaluating on linegroupware, collaborating viablogs and wikis
,Understanding cloud storage, exploring on line book marking services,
exploring on line photo editing applications, exploring photo sharing
communities, controlling it with web based desktops.
5. Amity Institute of Information Technology
GRADE âAâ ACCREDITED BY NAAC AUUP HQ
Amity Institute of Information Technology
⢠Pedagogy for Course Delivery:
⢠lectures in blended/flipped mode
⢠Assignments and Tutorials for continuous assessment
⢠Lab Assignments: NA
5
Pedagogy
6. Amity Institute of Information Technology
GRADE âAâ ACCREDITED BY NAAC AUUP HQ
Amity Institute of Information Technology
6
Self-
Work 1
Self-Work/ Professional Skill Development activities can be
conducted by dividing the class into group of 7-8 students and
same will be evaluated by board of faculty members along with
the group report. Some of the suggested activities are:
1. Theoretical understanding of data ware housing and cloud
computing services
2. Learning factor and dimension, data extraction with OLAP
operations
3. Describe the architecture of data warehouse and construction
of Data Warehouses and Data Marts
4. Design the diagram of data warehouse with proper example
Self-
Work2
1. Learn the different type of data mining and knowledge
2. Learning of different kind of CRM and its process
3. Describe the different kind of data mining application such as
fraud detection, data analytics in health care, banking and
finance, telecom, ecommerce, etc.
7. Amity Institute of Information Technology
Digital Marketing Overview
Assessment
7
L T P/S SW/FW TOTAL
CREDIT
UNITS
2 0 0 2 3
8. Amity Institute of Information Technology
Digital Marketing Overview
Assessment/ Examination Scheme:
8
Theory L/T (%) Lab/Practical/S
tudio (%)
End Term
Examination
100 NA 100
Continuous Assessment/Internal Assessment End Term
Examination
Components
(Drop down)
Attendance Class Test Assignment Case Study
Weightage
(%)
5 10 15 10 60
9. Amity Institute of Information Technology
Digital Marketing Overview
Text & References
⢠Michael Miller, âCloud Computingâ, Pearson Education, New Delhi, 2009.
⢠David S. Linthicum,â Cloud computing and SOA Convergence in your
Enterprise.
⢠Greg Schulz 2011, Cloud and Virtual Data Storage Networking, Auerbach
Publications [ISBN: 978-1439851739]
⢠EMC, Information Storage and Management [ISBN: 978-0470294215]
⢠Klaus Schmidt, High Availability and Disaster Recovery [ISBN: 978-
3540244608
9
10. Amity Institute of Information Technology
Digital Marketing Overview
Operating System
⢠An Operating System (OS) is an interface between a computer
user and computer hardware. An operating system is a software
which performs all the basic tasks like file management,
memory management, process management, handling input and
output, and controlling peripheral devices such as disk drives
and printers.
⢠Some popular Operating Systems include Linux Operating
System, Windows Operating System, VMS, OS/400, AIX,
z/OS, etc.
10
11. Amity Institute of Information Technology
Digital Marketing Overview
Functions of an operating System
⢠Memory Management
⢠Processor Management
⢠Device Management
⢠File Management
⢠Security
⢠Control over system performance
⢠Job accounting
11
12. Amity Institute of Information Technology
Digital Marketing Overview
Distributed Operating System
⢠Multiple central processors are used by Distributed systems to
serve multiple real-time applications and multiple users.
Accordingly, Data processing jobs are distributed among the
processors.
⢠Processors communicate with each other through various
communication lines (like high-speed buses or telephone lines).
These are known as loosely coupled systems or distributed
systems. Processors in this system may vary in size and
function. They are referred as sites, nodes, computers, and so
on.
12
13. Amity Institute of Information Technology
Digital Marketing Overview
DBMS
⢠Database Management System or DBMS in short refers to
the technology of storing and retrieving users data with utmost
efficiency along with appropriate security measures. This
tutorial explains the basics of DBMS such as its architecture,
data models, data schemas, data independence, E-R model,
relation model, relational database design, and storage and file
structure and much more.
13
14. Amity Institute of Information Technology
Digital Marketing Overview
Characteristics of DBMS
⢠Real-world entity â A modern DBMS is more realistic and uses real-world entities to design
its architecture. It uses the behavior and attributes too. For example, a school database may use
students as an entity and their age as an attribute.
⢠Relation-based tables â DBMS allows entities and relations among them to form tables. A
user can understand the architecture of a database just by looking at the table names.
⢠Isolation of data and application â A database system is entirely different than its data. A
database is an active entity, whereas data is said to be passive, on which the database works
and organizes. DBMS also stores metadata, which is data about data, to ease its own process.
⢠Less redundancy â DBMS follows the rules of normalization, which splits a relation when
any of its attributes is having redundancy in values. Normalization is a mathematically rich and
scientific process that reduces data redundancy.
⢠Consistency â Consistency is a state where every relation in a database remains consistent.
There exist methods and techniques, which can detect attempt of leaving database in
inconsistent state. A DBMS can provide greater consistency as compared to earlier forms of
data storing applications like file-processing systems.
⢠Query Language â DBMS is equipped with query language, which makes it more efficient to
retrieve and manipulate data. A user can apply as many and as different filtering options as
required to retrieve a set of data. Traditionally it was not possible where file-processing system
was used. 14
15. Amity Institute of Information Technology
Digital Marketing Overview
Characteristics and applications of
DBMS
⢠ACID Properties â DBMS follows the concepts of Atomicity, Consistency, Isolation, and Durability
(normally shortened as ACID). These concepts are applied on transactions, which manipulate data in
a database. ACID properties help the database stay healthy in multi-transactional environments and in
case of failure.
⢠Multiuser and Concurrent Access â DBMS supports multi-user environment and allows them to
access and manipulate data in parallel. Though there are restrictions on transactions when users
attempt to handle the same data item, but users are always unaware of them.
⢠Multiple views â DBMS offers multiple views for different users. A user who is in the Sales
department will have a different view of database than a person working in the Production
department. This feature enables the users to have a concentrate view of the database according to
their requirements.
⢠Security â Features like multiple views offer security to some extent where users are unable to access
data of other users and departments. DBMS offers methods to impose constraints while entering data
into the database and retrieving the same at a later stage. DBMS offers many different levels of
security features, which enables multiple users to have different views with different features. For
example, a user in the Sales department cannot see the data that belongs to the Purchase department.
Additionally, it can also be managed how much data of the Sales department should be displayed to
the user. Since a DBMS is not saved on the disk as traditional file systems, it is very hard for
miscreants to break the code.
15
16. Amity Institute of Information Technology
Digital Marketing Overview
Distributed Database Management System
⢠Distributed DBMS Tutorial. Distributed Database Management
System (DDBMS) is a type of DBMS which manages a
number of databases hoisted at diversified locations and
interconnected through a computer network.
⢠It provides mechanisms so that the distribution remains
oblivious to the users, who perceive the database as a
single database.
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Functions of Distributed Database
1. Cataloguing
2. Data Recoverability
3. Security
4. Distributed Query Processing
5. Data Transaction Management
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Advantages of DDBMS
⢠They are cost-effective and can drastically reduce database management costs by a fraction.
This helps a lot of startups and cash strapped companies to invest in other technologies.
⢠Itâs easily Scalable and therefore as the business grows, they can easily scale their distributed
system to handle an increased workload.
⢠The performance is drastically improved vis-a-vis a traditional single large computer system as
the data is located where the site has the greatest demand and the database systems can be
Parallelized allowing load distribution to be just and as per requirement.
⢠An Organization can better structure their various departments by better sorting of their
database systems where the data for a department is fragmented in their location.
⢠There can be a reliable transaction due to the replication of the database.
⢠Fault-tolerant and single site failure wonât affect the entire system.
⢠Local or central autonomy where there is greater flexibility with the organization and they can
choose who can access what data.
⢠It supports both OLTP and OLAP upon diversified systems that may have common data.
⢠Most organizations use various applications and with distributed systems, itâs robust enough to
use the same data under various applications.
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Disadvantages of DDBMS
⢠While it is economical to distributed systems, in the long run,
its installation can be expensive as it would need relatively high
resources to set up a distributed system effectively.
⢠The need for updating data on every site can sometimes affect
data integrity.
⢠The entire system can become unresponsive or slow if data is
not properly distributed and work is not handled correctly. This
again can also increase overheads as personnel would need to
be available to monitor the system constantly.
⢠Organizations wanting to convert from a centralized database
to distributed systems also might face problems due to lack of a
standard procedure as there are no tools or methodologies for
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Digital Marketing Overview
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