Introducing NoSQL Databases

What are NoSQL databases?

NoSQL database is a type of database that stores and manages data differently from traditional relational (SQL) databases. The term NoSQL means “not only SQL” (a.k.a. non-SQL), showing that these databases offer other ways to work with data beyond just tables and rows.

Popular NoSQL databases include MongoDB, Cassandra, Redis, Amazon DynamoDB, Neo4j, Elasticsearch, and Firebase Realtime Database. 

When should you use NoSQL databases?

NoSQL databases are ideal when:

• Our data is dynamic or evolving, e.g., when we’re building applications like real-time analytics, content management, IoT, or social networks.

• We need to store data that doesn’t have a clear structure, such as images, videos, audio files, emails, or social media posts.

• Our application requires high throughput This refers to the amount of data or tasks a system can handle in a given period of time.and low latencyIt refers to the ability of a system to ensure fast response times for user interactions and data processing..

• We need to scale horizontallyThis means adding more machines (servers) to handle increased data or traffic. across many servers.

NoSQL databases are flexible. Based on the application's needs, they allow storing data in different formats like documents, key-value pairs, wide-columns, or graphs, without needing a fixed table structure. Known for their high performance, NoSQL databases are optimized for fast read and write operations, making them ideal for real-time applications. Additionally, these databases can scale easily without relying on a single, powerful server.

SQL vs. NoSQL: Key differences

Consider storing information about a person, such as their name and age. Let’s look at a visual example to understand the basic difference between SQL and NoSQL databases.

For SQL, we can see that the data is stored in a table format, like a spreadsheet. Each row represents a person. Each column represents a field: ID, Name, and Age. We have example rows as follows:

• Row 1: ID = 1, Name = John Smith, Age = 23

• Row 2: ID = 2, Name = Bob Will, Age = 25

Now, let's see the NoSQL representation.

Here, data is stored as JSON-like documents. Let's take a closer look at them:

• NoSQL document 1: It’s a simple document similar to the SQL row. The id, name, and age are stored as a single string.

• NoSQL document 2: This document shows nested data. The name field is broken into first and last, which is something SQL databases cannot do without creating another table.

It means that in NoSQL databases, each document can have different fields, and new fields can be added to a document without affecting other documents. The table below summarizes the difference between SQL and NoSQL databases.

Advantages and disadvantages of NoSQL

Based on the aforementioned concepts, the advantages of NoSQL databases can be summarized as follows:

• Flexible data models: These models can easily handle unstructured or semi-structured data.

• Scalability: NoSQL databases are designed to scale out across many servers.

• Performance: They are optimized for high-speed operations and large datasets.

• Variety: NoSQL databases come in multiple types (document, key-value, graph, column-family), allowing developers to choose the best fit for their use case.

NoSQL databases come with their own set of limitations. A few of them are listed below:

• Lack of consistency: Some NoSQL databases may temporarily show outdated data because they focus on speed and scalability.

• No standard language: Unlike SQL for relational databases, there isn't any standard way to query all NoSQL databases.

• Complex transactionsIt is a series of one or more data operations (like reading, writing, updating, or deleting data).: With NoSQL, handling multiple pieces of data can be challenging.

• Less mature: Some NoSQL databases are relatively new and may not have all the features or support of older, well-known databases.

However, NoSQL databases offer flexible and scalable solutions for modern data needs, especially when traditional relational databases fall short.