How to Choose the Right Type of Database for your Enterprise?
Choosing an appropriate database to meet the needs of your enterprise technology could sound overwhelming. In order to proceed with the selection of an appropriate database for the enterprise, it is essential to begin by understanding what databases are available. It is true that the correct database is actually a long-term decision that can influence your business. Also, it is useless to begin working on database implementation that will outgrow your enterprise’s business in a few months.
Many people usually misunderstand that databases are just about rows and columns. Whether you are scaling a small SaaS business or a gigantic Fortune 500 company, it is confirmed that you would need a proper methodology to store all data, ranging from user IDs to email marketing.
Have you ever wondered how a system is capable to identify your enterprise and remember all the data, based on just username and password? It is being made possible with the help of the database that consists of all the data and information whenever you register for any application.
Benefits of choosing the right type of database to the enterprise:
The choice of the right type of database will facilitate storage of relevant and the necessary information into the database. This will help the storage of data in a consistent manner.
It helps to normalize the database design. This will assist to decrease data redundancy and also avoids duplication of data. In addition to that, it will also help to decrease the size of the database.
The appropriate choice of the database will simplify the execution of queries fired for fetching the data and also it will speed up the execution.
The database related tasks of the enterprise will perceive easy maintenance.
Factors to be considered while choosing the right database for your enterprise:
Let’s have a look at the important factors to be taken into account while choosing the right database for your enterprise:
Size of data to be stored:
This factor takes care of the quantity of data an enterprise will require for storage and retrieval purpose. For any enterprise, the amount of data it can store and retrieve might differ based on the combination of the data structure chosen, the capability of the database to distinguish data over various servers and file systems, and vendor-specific optimizations.
Accessibility of data:
The number of users simultaneously using the database as well as the level of computation incorporated for accessing a particular data is an essential factor to take care of while selecting the right database. Moreover, the application’s processing speed gets influenced if the selected database improperly handles a huge load.
Structure of data:
The way your enterprise requires storage and retrieval of data depends on the structure of the data. The application in enterprise handles data existing in many different forms. Therefore, choosing the appropriate database must incorporate going for the correct data structures for the purpose of storage and retrieval of data.
Safety of data:
In addition to the above aspects, it is also necessary that your enterprise check for the level of security provided to the data stored in it by a particular database. When the need is such that the data intended to be stored is extremely confidential, there is a requirement of a highly secured database. In situations of any system crash or failure, the safety measures executed by the particular database are very essential factors to keep in mind when selecting a database.
Now let’s have a look at different databases for the enterprise:
Relational database management systems:
Back in the 1970s, relational databases were developed, in order to manage the rising flood of data that are produced. The relational database management systems (RDBMS) owns a solid foundational theory, also it has affected almost all database system that is used currently. Examples of RDBMS include Oracle, MySQL, PostgreSQL, and MS Server.
Relational databases function to store data sets in form of “relations”: tables consisting of columns and rows. The tables store all the information in form of a value of a specific cell. The data is then handled with the help of SQL. It is true that there are many varied implementations but SQL is consistent and offers a great level of utility and predictability.
Relational databases are capable to handle highly structured data. Moreover, it also offers great support for ACID (Atomicity, Consistency, Isolation, and Durability) based transactions. When your enterprise uses it, the result is data gets easily saved and the same can be easily retrieved with the help of SQL queries. Furthermore, the structure could be conveniently scaled up swiftly due to the fact that supplementing data without altering existing data is easy.
These kinds of databases are proficient at handling structured data but they found some difficulty with unstructured data. It is found that signifying real-world entities in this respect is tough. Moreover, the cost is too a major consideration for relational databases because they are usually costly to set up and develop.
An enterprise can use a relational database in the following cases:
Situations where data integrity is supreme like for defense and security, financial applications, and private health information.
For the automation of internal processes
For availing highly structured data
Wide-column stores (alternatively known as column stores or extensible record stores) are basically dynamic column-oriented non-relational types of databases. Occasionally, they are observed as a type of key-value store; however, they have characteristics of conventional relational databases too.
It is found that a Wide-column stores database implements the idea of a keyspace and not schemas. In this respect, a keyspace covers column families (identical to tables but it is flexible in structure), and every one of it comprises of different rows with separate columns. Examples include Cassandra and HBase.
The wide-column store database has many advantages as compared to relational as well as non-relational databases. It has the capability to deal with structured as well as semi-structured data as compared to several other non-relational types of databases. The best aspect is that it is simple to update. When compared to relational databases, this type of database is horizontally scalable and quicker at scale.
Though the advantages of this database are many it comes with certain limitations. It is found that writes are costly. The process of updating is simple to do in bulk but tasks related to updating and uploading distinct records are difficult. In addition to that, wide-column stores are found to be comparatively slower than relational databases while managing transactions. The wide-column store database can be used for big data analytics where the significant aspect is speed, data warehousing on big data, and for large-scale projects.
A key-value store is recognized as a non-relational type of database wherein every value is linked with a precise key. The other name of this database is an associative array. In this database, values get stored as blobs and they do not require a predefined schema. Moreover, they can be taken almost all the forms like counters, strings, numbers, XML, HTML, JSON, PHP, images, binaries, lists, short videos, etc. Examples of key-value store databases include Redis and Memcached.
The database comes with many advantages. First of all, it is very flexible and capable to handle an extensive array of data types effortlessly. The performance is high because the keys are utilized to reach directly to the value with no joins or index searching. Apart from that, portability is an additional plus point of this database as it can be transferred from one system to the next, eliminating the requirement of rewriting code. They are extremely horizontally scalable and they even come with reduced operating costs.
The negative aspect of this database is that it is not possible to query values. This is due to the fact that they are effectively stored as a blob and they can only be returned. This aspect lets it difficult to do reporting or alter parts of values. An enterprise can use it for recommendations, user profiles, and settings, unstructured data like blog comments or product reviews.
A document store is a non-relational kind of database that is capable of stores data in BSON, JSON, or XML documents. Contrasting SQL databases, in which users should define a table’s schema prior to data insertion, document stores actually do not implement document structure. Examples include Couchbase and MongoDB.
The positive aspect of this database is that they are very flexible. They are capable to control semi-structured as well as unstructured data. Users need not know about the type of data to be stored during its set-up. This proves to be a good choice. It is found that users get the flexibility to create their desired structure in a specific document without influencing other documents. Though the plus points of the document store database are many, it comes with some limitations. The database compromises with ACID compliance for the purpose of flexibility. Enterprises usually use this database for in-depth data analysis, unstructured or semi-structured data, content management, and quick prototyping.
With an increasing number of data handling uses, the enterprise needs an excellent database for data storage and retrieval. They can use any of these databases to effectively manage the data.
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