In an era where there are so many types of Databases available it's difficult to choose the best one for your project. In this article, we shall see at first what kind of database will be best for your project’s requirements.

We will mostly focus on NoSQL and how it works, in this article as it is most often the best choice for rapid development with tighter time constraints or where the solution or product needs constant scaling, fast queries, allowing for frequent application changes which mean constantly changing the database and its components.

What is a NoSQL database?

NoSQL databases (aka “not only SQL”) are non-tabular and store data differently than relational tables. NoSQL databases come in a variety of types based on their data model. The main types are document, key-value, wide-column, and graph.

These databases store data in a format other than relational tables. They provide flexible schemas and scale easily with substantial amounts of data and high user loads.

A common misconception is that NoSQL databases or non-relational databases don’t store relationship data well. NoSQL databases can store relationship data — they just store it differently than relational databases do.

In fact, when compared with SQL databases, many find modelling relationship data in NoSQL databases to be easier than in SQL databases, because related data doesn’t have to be split between tables.

As storage costs rapidly decreased, the amount of data applications needed to store, and query increased. This data came in all shapes and sizes — structured, semi structured, and polymorphic — and defining the schema in advance is impossible.

NoSQL databases allow developers to store massive amounts of unstructured data, giving them a lot of flexibility.

Then What is a SQL database?

Now that we understand NoSQL databases, let’s contrast them with what have traditionally been the most popular databases: relational databases accessed by SQL (Structured Query Language). You can use SQL when interacting with relational databases where data is stored in tables that have fixed columns and rows.

SQL databases rose in popularity in the early 1970s. At the time, storage was extremely expensive, so software engineers normalized their databases to reduce data duplication.

During the 1970s, many software engineers started with the waterfall software development model. They planned their projects in detail, creating complex entity-relationship diagrams along the way, which prevented software engineers from adapting to changes in the requirements during the development cycle. As a result, projects frequently went over budget, exceeded deadlines, and failed to deliver against user needs.

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What are the Types of NoSQL Databases?

NoSQL databases offer many benefits over relational databases. NoSQL databases have flexible data models, scale horizontally, have incredibly fast queries, and are easy for developers to work with.

Over time, four major types of NoSQL databases emerged: document databases, key-value databases, wide-column stores, and graph databases. Let’s examine each type.

• Document databases store data in documents like JSON (JavaScript Object Notation) objects. Each document contains pairs of fields and values. The values can typically be a variety of types, including strings, numbers, Booleans, arrays, or objects. Their structures typically align with objects developers are working within code. Because of their variety of field value types and powerful query languages, document databases are great for a wide variety of use cases and can be used as a general-purpose database. They can horizontally scale out to accommodate large data volumes. MongoDB and Cloud FireStore are popular document databases.
• Key-value databases are a simpler type of database where each item contains keys and values. A value can typically only be retrieved by referencing its key, so learning how to query for a specific key-value pair is typically simple. Key-value databases are great for use cases where you need to store substantial amounts of data, but you don’t need to perform complex queries to retrieve it. Common use cases include storing user preferences or caching. Redis and DynamoDB are popular key-value databases.
• Wide-column stores store data in tables, rows, and dynamic columns. It provides a lot of flexibility over relational databases because each row is not required to have the same columns. Many consider wide-column stores to be 2-D key-value databases. Wide-column stores are great for when you need to store enormous amounts of data and you can predict what your query patterns will be. Wide-column stores are commonly used for storing Internet of Things data and user profile data. Cassandra and HBase are two of the most popular wide-column stores.
• Graph databases store data in nodes and edges. Nodes typically store information about people, places, and things while edges store information about the relationships between the nodes. They can run smoothly across various scenarios where you need to find patterns in data such as social networks, fraud detection, and recommendation engine use cases. Neo4j and Janus Graph are examples of graph databases.

How NoSQL Databases Work

One way of understanding the appeal of NoSQL databases from a design perspective is to look at how the data models of a SQL and a NoSQL database might look in an oversimplified example using address data.

The SQL Case → For an SQL database, setting up a database for addresses begins with the logical construction of the format and the expectation that the records to be stored are going to remain unchanged. After analyzing the expected query patterns, an SQL database might optimize storage in two tables, one for basic information and one about being a customer, with the last name being the key to both tables.

The NoSQL Case → In the section Types of NoSQL Databases above, there were four types described, and each has its own data model.

Each type of NoSQL database would be designed with a specific customer situation in mind, and there would be technical reasons for how each kind of database would be organized. The simplest type to describe is the document database, in which it would be natural to combine both the basic information and the customer information in one JSON document.

TL; DR

Tell me the Key Differences between SQL and NoSQL

All this is fine! But what are its drawbacks I should know before getting started?

• One of the most frequently cited shortcomings of NoSQL databases is that they do not support ACID (atomicity, consistency, isolation, durability) transactions across multiple records. This is, however, an acceptable requirement for many applications.
• Since databases in NoSQL are optimized for query-oriented data, not for reducing data duplication hence they can be larger than SQL databases. Storage is currently so cheap that most consider this a minor drawback, and some NoSQL databases also support compression to reduce the storage footprint.
• Depending on the type of database you select, you may not get the full benefits of a single database. For instance, graph databases are good for analyzing data but may not provide the necessary tools for everyday operations.

Both the SQL and NoSQL databases have their use cases across different applications and scenarios, so you may have to choose multiple databases to support your applications.

Hope it helped you to figure out the information you came looking for. 😃😃

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