Database Selection Made Easy: Understanding the 7 Paradigms to Choose the Best Suitable Database for Your Business Needs
The most crucial and important choice to be made before starting any new project is selecting the right database. Because future consequences of a bad choice could negatively affect your project. We may choose the optimal database model from 7 different database paradigms by considering the following characteristics to reduce this potential impact.
- Query pattern
One of the widely used methods for selecting a specific database depends on how complicated your queries are. For example, if you are fetching data based on the keys, all you need is a simple key-value database. - Ease of modifying database schema
Certain databases demand a fixed, established schema, which over time becomes complex and difficult as you are required to update it often in accordance with requirements. NoSQL databases, however, are significantly more adaptable because they don’t need any schema. - Consistency
Retrieving the most recent written data. Usually, relational databases have strong consistency over non-relational databases. - Scalability
If your data is expected to grow uncontrolled over time, scaling a database effectively is essential. relational databases cannot be clustered and scaled effectively. While key-value and document-based databases are easier to deploy across clusters and scale efficiently. - Performance
Database performance can be considered as how it responds to the traffic of read/write queries and latency between their execution. - Storage capacity
How much data a database can hold? Some databases might be restricted by the amount of disc space available. whereas some may increase capacity based on requirements. - Cost
Saving a penny will definitely help instead of spending it on unnecessary services. So, choose the database wisely.
Database Paradigms
Key-Value Database
The Data is stored as a set of key-value pairs where every key is unique and points to the associated value. The structure of a key-value database is similar to a Python dictionary or a Javascript object.
Wide-Column Database
In a wide-column database, each unique key points to one or more columns of key-value pairs, just as if we had added an additional dimension to the values of a key-value database.
This unstructured database can be interacted via query language such as CQL.
Document-Oriented Database
It stores data as key-value pairs in JSON, BSON, or XML documents, which are subsequently grouped together to form collections that can be arranged in a logical hierarchy.
Relational Database
It keeps data as tables in the form of columns and rows. Tables are then linked with each other based on related data using primary keys and foreign keys.
SQL (Structured Query Language) is used to interact with Relational databases.
Graph Database
It stores data in the form of nodes, and edges show how two nodes are related to one another. When data has complex many-to-many relationships, Graph Database is very helpful.
Search Database
It stores data same as document-oriented databases, which begin with an index and add data objects as documents to it. Search database will analyze all the text in documents and create an index of searchable items. Basically, It is similar to the index in the back of a textbook.
Multi-Model Database
The multi-model database combines various types of database models into a single integrated database engine that offers a single backend and supports various database paradigms internally depending on the type of data.
