This document discusses trends in database management. It describes different types of databases including operational databases, analytical databases, data warehouses, distribution databases, columnar databases, data warehouse appliances, in-memory databases, embedded databases, document oriented databases, graph databases, hypermedia databases, and flat file databases. It outlines the key characteristics and purposes of each type of database. The document also covers learning outcomes which are to define and explain embedded databases and to identify, compare, and describe document oriented databases, graph databases, hypermedia databases, and flat file databases.
Relational databases have pretty much ruled over the IT world for the last 30 years. However, Web 2.0 and the incipient Internet of Things (IoT) are some of the sources of a data explosion that has proved to exceed the limits of what modern relational databases can handle in a growing number of cases. As a result, new technologies had to be developed to handle these new use cases. We generally group these technologies under the umbrella of Big Data. In this two part presentation, we will start by understanding how relational databases have evolved to become the powerhouses they are today. In part 2 we will look at how non SQL databases are tackling the big data problem to scale beyond what relational databases can provide us today.
Relational databases have pretty much ruled over the IT world for the last 30 years. However, Web 2.0 and the incipient Internet of Things (IoT) are some of the sources of a data explosion that has proved to exceed the limits of what modern relational databases can handle in a growing number of cases. As a result, new technologies had to be developed to handle these new use cases. We generally group these technologies under the umbrella of Big Data. In this two part presentation, we will start by understanding how relational databases have evolved to become the powerhouses they are today. In part 2 we will look at how non SQL databases are tackling the big data problem to scale beyond what relational databases can provide us today.
Implementation of Big Data infrastructure and technology can be seen in various industries like banking, retail, insurance, healthcare, media, etc. Big Data management functions like storage, sorting, processing and analysis for such colossal volumes cannot be handled by the existing database systems or technologies. Frameworks come into picture in such scenarios. Frameworks are nothing but toolsets that offer innovative, cost-effective solutions to the problems posed by Big Data processing and helps in providing insights, incorporating metadata and aids decision making aligned to the business needs.
Scholars and researchers are being asked by an increasing number of research sponsors and journals to outline how they will manage and share their research data. This is an introduction to data management and sharing practices with some specific information for Columbia University researchers.
I will discuss the growth of big data and the evolution of traditional enterprise models with addition of critical building blocks to handle the intense development of data in the enterprise. According to IDC approximations the size of the digital universe in 2011 will be 1.8 zettabytes. With statistics evolution beyond Moore’s Law the average enterprise will need to manage 50 times more information by the year 2020 while cumulative IT team by only 1.5 percent. With this challenge in mind, the combination of big data models into existing enterprise infrastructures is a critical element when seeing the addition of new big data building blocks while bearing in mind the efficiency.
Summary:
A database, often abbreviated as DB, is a collection of information organized in such a way that a computer program can quickly select desired pieces of data.
You can think of a traditional database as an electronic filing system, organized by fields, records, and files.
A field is a single piece of information; a record is one complete set of fields; a file is a collection of records.
Eliminating the Problems of Exponential Data Growth, Foreverspectralogic
Balancing explosive data growth while addressing the need for extended data protection is mandatory for any IT department. But customers today find it difficult to address these challenges because of the software management layers and tools required in order to meet longer retention mandates. While exponential data growth is not a new problem, the quandary that IT faces in 2014, now has a new solution.
Join Spectra and IDC as we identify the greatest dilemmas facing data centers in 2014, and explore the capabilities of Spectra’s newest product, the BlackPearl™ Deep Storage Appliance. During this brief webinar, attendees will learn about:
-A situation analysis of today’s software-defined data center
-How moving to an “elastic” data center enables more cost-effective and efficient data management
-Emerging technologies and key strategies to store and manage data indefinitely
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Implementation of Big Data infrastructure and technology can be seen in various industries like banking, retail, insurance, healthcare, media, etc. Big Data management functions like storage, sorting, processing and analysis for such colossal volumes cannot be handled by the existing database systems or technologies. Frameworks come into picture in such scenarios. Frameworks are nothing but toolsets that offer innovative, cost-effective solutions to the problems posed by Big Data processing and helps in providing insights, incorporating metadata and aids decision making aligned to the business needs.
Scholars and researchers are being asked by an increasing number of research sponsors and journals to outline how they will manage and share their research data. This is an introduction to data management and sharing practices with some specific information for Columbia University researchers.
I will discuss the growth of big data and the evolution of traditional enterprise models with addition of critical building blocks to handle the intense development of data in the enterprise. According to IDC approximations the size of the digital universe in 2011 will be 1.8 zettabytes. With statistics evolution beyond Moore’s Law the average enterprise will need to manage 50 times more information by the year 2020 while cumulative IT team by only 1.5 percent. With this challenge in mind, the combination of big data models into existing enterprise infrastructures is a critical element when seeing the addition of new big data building blocks while bearing in mind the efficiency.
Summary:
A database, often abbreviated as DB, is a collection of information organized in such a way that a computer program can quickly select desired pieces of data.
You can think of a traditional database as an electronic filing system, organized by fields, records, and files.
A field is a single piece of information; a record is one complete set of fields; a file is a collection of records.
Eliminating the Problems of Exponential Data Growth, Foreverspectralogic
Balancing explosive data growth while addressing the need for extended data protection is mandatory for any IT department. But customers today find it difficult to address these challenges because of the software management layers and tools required in order to meet longer retention mandates. While exponential data growth is not a new problem, the quandary that IT faces in 2014, now has a new solution.
Join Spectra and IDC as we identify the greatest dilemmas facing data centers in 2014, and explore the capabilities of Spectra’s newest product, the BlackPearl™ Deep Storage Appliance. During this brief webinar, attendees will learn about:
-A situation analysis of today’s software-defined data center
-How moving to an “elastic” data center enables more cost-effective and efficient data management
-Emerging technologies and key strategies to store and manage data indefinitely
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
3. Z
Review: Trends in Database
Management
• Used to manage dynamic data in real
time and stores information about the
activities of an organization.
a. Operational Database
b. Analytical Database
c. Data Warehouse
d. Distribution Database
4. Z
Review: Trends in Database
Management
• It is designed to support business
intelligence and analytic applications,
typically as part of data warehouse or data
mart.
a. Operational Database
b. Analytical Database
c. Data Warehouse
d. Distribution Database
5. Z
Review: Trends in Database
Management
• It stores current and historical data and
are used for creating analytical reports for
knowledge workers through the enterprise.
a. Operational Database
b. Analytical Database
c. Data Warehouse
d. Distribution Database
6. Z
Review: Trends in Database
Management
• It organizes data by column instead of
rows – thus reducing the number of data
elements that typically have to be ready by
the database engine while processing
queries.
a. Columnar Database
b. Data Warehouse Appliance
c. In Memory Database
d. MMP Database
7. Z
Review: Trends in Database
Management
• It combines the databases with hardware
and BI tools in an intelligent platform
that’s turned for analytical workloads and
designed to be easy to install and operate.
a. Columnar Database
b. Data Warehouse Appliance
c. In Memory Database
d. MMP Database
8. Z
Scope of the Lesson
• Trends in Database Management
• Embedded Database
• Document Oriented Database
• Graph Database
• Hypermedia Database
• Flat File Database
9. Z
Learning Outcomes
By the end of the lesson, you will be
familiar with the current trends in the database.
• Define and explain perception of the
embedded database
• Identify and compare the dynamics of the
document oriented database and graph database
• Describe the features and the aim of the
hypermedia database and a flat file.
10. Z
Embedded Database
• Embedded Database: these databases consist
of data developed by individual end-users.
Examples of these are collections of
documents, spreadsheets, presentations,
multimedia and other files.
12. Z
Document Oriented Database
• Document Oriented Database: the document
oriented database is a computer program
designed for storing, retrieving, and managing
document oriented or semi structured data
information.
14. Z
Graph Database
• Graph Database: is a kind of No SQL
database that uses graph structures with nodes,
edges and properties to represent and store
information.
16. Z
Hypermedia Database
• Hypermedia Database: is a computer
information retrieval system that allows a user
to access and work on audio-visual recordings,
text, graphics and photographs of a stored
subject.
• The world wide web is a perfect example of a
hypermedia database.
17. Z
Hypermedia Database
• Hypermedia Database: Example
• The biggest advantage of hypermedia
databases as compared to traditional databases
is that documents are accessed via organized
links. Examples of hypermedia database
products in today’s market are Visual FoxPro
and FileMaker Developer. These brands of
software are excellent for creating business and
management content.
18. Z
Hypermedia Database
• Hypermedia Database: Example
• The Web is a type of hypermedia database
because it provides results for all available
media of a phenomenon. For example, if a user
types the word "vehicle" on a search engine, it
gives results of various media that "vehicle"
falls under. Records of items are stored
according to the subject of the file.
19. Z
Flat File Database
• Flat File Database: is a database which, when
not being used is stored on its host computer
system as ordinary, non-indexed flat file.
• To access the structure of the data and
manipulate it, the file must be read in its
entirety into the computer’s memory.
20. Z
Advantages of
Flat File Database
• Placing data in flat file database has the
following advantages:
• All records are stored in one place.
• Easy to understand
• Easy to set up using a number of standard
office applications.
• Simple sorting of records can be carried
out.
21. Z
Disadvantages of
Flat File Database
• Flat file has serious disadvantages when it
comes to more than a few thousands of records.
22. Z
Disadvantages of
Flat File Database
• Potential Duplication: As more and more
records are added to the database, it becomes
difficult to avoid duplicate records.
• Non-unique Records: Notice that Mr. & Mrs.
Jones have identical ID's. This is because the
person producing this database decided they may
want to sort on identical telephone numbers and
so has applied identical ID to the two records.
This is fine for that purpose, but supposes you
only wanted to extract Mrs. Jones' record. Now it
is much more difficult.
23. Z
Disadvantages of
Flat File Database
• Harder to Update: suppose that this is a flat
file database also stored their work place details
– this will result in multiple records for each
person. Again, this is fine but suppose Sandra
Jones now wanted to be known as “Sandra
Thompson” after re-marrying? This will have
to be done over potentially many records and so
flat file updates are more error prone than other
methods.
24. Z
Disadvantages of
Flat File Database
• Inherently inefficient: consider a situation
where the database now needs to hold an extra
field to hold an email address. If there are tens
of thousands or records, there may be many
people having no email address, but each
record in a flat file database has to have the
same fields, whether they have used or not.
Other methods avoid this wasted storage.
25. Z
Disadvantages of
Flat File Database
• Harder to Change Data Format: Suppose the
telephone numbers now have to have a dash
between the area code and the rest of the
number, like this 0223-44033. Adding that
extra dash over tens of thousands of records
would be a significant task in a flat file
database.
26. Z
Disadvantages of
Flat File Database
• Poor at Complex Queries: If we wanted to
find all records with a specific telephone
number, this is a simple single-field criterion
that a flat file can easily deal with. But now
suppose we wanted all people living in Hull
who share the same surname and similar
postcode? - the criteria can quickly become too
complex for a flat file to manage.
27. Z
Disadvantages of
Flat File Database
• Poor at Limiting Access: Suppose this flat file
database held a confidential field in each record
that only certain staff are allowed to see -
perhaps salaries. This is difficult to achieve in a
flat file database - once a person has entered a
valid password to gain access, that person is
able to see everything.