Advanced Databases: Their Needs and Importance

Advanced Databases are becoming more wild, advantageous and applicable to real life as developers of these data bank strive to make that happen. In this article, I give an overview of several Advanced Databases and explain why they are important

Here I cite three such kinds of data bank:

  1. Distributed Data bank

A distributed database is a database with one common schema whose parts are physically distributed via a network. For a user, a distributed database may seem like a central Latest Mailing Database database i. e. it is cannot be seen to users where each data item is actually located. However, the database management system (DBMS) must periodically synchronize the dotted data bank to make sure that they have all consistent data.

Advantages:

Reflects organizational structure: database pieces are located in the section they relate to.
Local autonomy: a department can control the data about them (as they are the ones familiar with it)
Improved availability: a fault in one database system will affect one fragment rather than the entire database.
Improved performance: data is near the site of greatest demand; the database systems themselves are parallelized, allowing load on the data bank to be balanced among servers. (A high load on one component of the database won’t affect other segments of the database in a distributed database)
Ergonomics: It costs less to manufacture a network of smaller computers with the power of a single large computer.
Modularity: Systems can be modified, added and taken out of the distributed database without which affects other segments (systems).

  1. Data Warehouses

A data warehouse (DW) is a subject-oriented, integrated, non-volatile and time-variant bunch of data for management’s decisions. (Inmon’s definition).

Explanation:

Subject-oriented: The machine focus is not on the applications required by the different section of a company (e. gary the gadget guy. econometrics and finance, medical research and biotechnology, data mining, engineering etc) but on subject areas, those that relate to all section like customers, products, profits etc. Traditional database systems are developed for the different applications and data warehouses for the subject areas.
Integration: Data from various sources is represented in the data warehouse. Different sources often use different promotions in which their data is represented. It must be unified to be represented in a single format in the data warehouse. E. gary the gadget guy., Application A uses “m” and “f” to denote gender. Application B uses “1” and “0” and application C uses “male” and “female”. One of the promotions can be used for the data warehouse; others can be turned.
Non-volatility: Data that have moved into the DW are not changed or taken out.
Time-variance: DW data is stored in a way to allow comparisons of data loaded at different times (e. gary the gadget guy. a company’s profits of last year versus the profits of the year before that). DW is like a series of snapshots of the data of its different sources, taken at different times, over a long period of time (typically 5-10 years).
The reason for most data bank is to present current, not historical data. Data in traditional data bank is not always associated with a time whereas data in a DW always is.

Advantages:

Because DW is subject-oriented, it deals with subject areas like customers, products and profits relating to all section of a company but not to different applications relating to different section.
It converts non-homogeneous data to homogeneous data.
Data do not require to be updated or taken out. It can be stored redundantly.
It can present historical data over a period of 5-10 years. So it can be used for the purpose of analysis of data.

  1. Multimedia Data bank

Multimedia data bank store multimedia such as images, audio and video. The database functionality becomes important when the number of multimedia objects stored is large.

Advantages:

The database supports large objects since multimedia data such as videos can occupy up to and including few gigabytes of storage.
Similarity-based access work extremely well in many multimedia database applications. For example, in a database that stores fingerprint images, a query fingerprint is provided, and the fingerprint(s) in the database that act like the query finger print are retrieved.
The access of some types of data such as audio and video has the requirement that data delivery must proceed at a guaranteed steady rate. This is a good upside as for example, if audio data are not supplied in time, there will be holes in the sound. If data are supplied too fast, system buffers may overflow resulting in loss of data.
These are a lot of the Advanced Databases that are taking bigger roles in real life, and their abundant merits make them even more an important part of data storage, access and applicability someone’s next to conventional relational data bank.

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