When senior enterprise people think of databases, they often visualize only the traditional relational database models, which involve structured tables with rows and columns.
Even though relational DBMS can still handle a significant share of the data across global enterprises, there are many alternative data models also getting popularized in recent times.
Developers and database administrators are seeking more options to work around the limitations of the relational database models.
There are non-relational database models, each of which has unique advantages and disadvantages in specific use cases. Non-relational databases are categorized as no SQL databases, which do not follow the table-based database formats.
This article will discuss some commonly used NoSQL database models and their strengths and disadvantages against the conventional relational model of database management.
Limitations of relational databases
As we know, our databases are a cluster of information that is logically modeled for storage and retrieval. A DBMS is a program, which interacts with databases and fetches data.
A good DBMS will allow users to control access to a database, write and read data, run a query, and easily perform any database management-related tasks. Even though database management systems are commonly referred to as databases, these terms are not fully interchangeable.
A simple database can be a collection of various types of data stored on a server, whereas DBMS is a complete application that allows you to interact with the stored data.
All the database management systems may have underlying models with structures as to how the data is stored and retrieved. A relational database management system or RDBMS employs a relational model in which the data is organized into tables.
Relational database management systems employ SQL or structured query language for managing and accessing data within it.
Historically, the relational model has been one of the most widely used approaches to database management, and until now, it has remained so. However, the relational model comes with many limitations, which are problematic in the modern-day use cases of big data, IoT, machine learning, and data cloud.
Let us take a simple instance as it may be challenging to scale the relational DB horizontally. So, to scale up, you need to add more machines and servers to the existing stack to accommodate more traffic and faster processing.
Choosing an apt database model is a challenging task in enterprise database planning, for which you can take the assistance of reliable consultants.
Introduction of NoSQL
NoSQL was a term coined back in 1998 as a new category of non-relational database management systems, which does not use SQL to manipulate data. NoSQL now generally refers to all types of DBMS which do not employ a relational model.
There are various operational data models associated with NoSQL. Here are some of the data models involved in the NoSQL portfolio:
- Key-value stores.
- Columnar databases.
- Document stores.
- Graph databases.
Apart from this underlying data model for most of the cases, databases also share various common characteristics. NoSQL databases are designed typically to optimize availability at the expense of consistency. In this approach, consistency means that the read operations may return the most accurate data needed from the database.
In a distributed database model with solid consistency, any data on one node may immediately be available to all other nodes in the network; otherwise, it will fail as a whole.
The NoSQL model aims at eventual consistency. This means that the freshly returned data is immediately made available for the other nodes in the database in a matter of milliseconds. This is beneficial to improve the availability of data.
Even though you cannot see the latest data return, we can still view the immediate previous version instead of returning an error.
The relational databases are meant to deal with a normalized form of data, which can fit neatly into the predefined schema. In the DBMS context, normalize data is the data that has been organized in specific ways to eliminate any redundancies.
This means that the database will take up only very little storage space as possible with the schema as to how the data in the database will be structured.
NoSQL databases are also equipped to handle normalized or structured data. These DBs can sort data within a predefined schema. However, their respective data models will allow greater flexibility than the rigid structure followed by the relational databases.
Because of this flexibility, NoSQL databases also have a reputation for being a better choice for storing unstructured or semi-structured data. Keeping this in mind, even though NoSQL databases do not come with a predefined schema, it often means it is up to the administrators to define how the data needs to be organized and accessed in whatever possible ways.
These are also known as key-value stores, as these work by storing and managing the associated arrays. The dictionary is a collection of key-value pairs in which the key server is the unique identifier to retrieve and associate values.
The values can be anything from basic objects like strings to more complex objects like JSON structures.
Columnar databases are also known as column-oriented databases, which are systems storing data in the form of columns. It may seem similar to the relational database model.
Rather than grouping the columns into tables, each column is kept as a separate file or region in the storage.
In document-oriented databases or document stores, the data is stored in the form of documents. Document stores are also the types of key-value stores, in which each document has a unique identifier as the key, and the document itself serves as the value.
Graph databases can be considered as the subcategory of the document model. In this, data is stored in documents, and data adhere to any predefined schema. The difference is that graph databases add an additional layer to the document model by highlighting the relationship between various documents.
Based on your use case, one can choose among RDBMS and NoSQL databases. For properly structured data, it is ideal to do the relational database model, and for unstructured or semi-structured data, you can think of the NoSQL data models.