Modern Database: From SQL to NoSQL

Introduction

This article is a continuation of my previous discussions on the evolution of modern databases. In my previous article, I covered the development of relational databases, such as MySQL, which are based on SQL and widely used for storing customer lists, product inventories, and sales transactions. However, as more and more daily activities moved online, the traditional SQL-based relational database struggled to keep up with the large volumes of data generated by modern software applications, including email and social media.

When Facebook wanted to offer its users the ability to search their inboxes, it became clear that a new approach was needed to manage the massive amounts of unstructured data generated by such applications. This need, along with others, led to the development of NoSQL databases. Unlike relational databases, NoSQL databases are designed to handle large amounts of unstructured data and are well-suited to handle the demands of modern software applications.

In this article, I will discuss various types of data, the difference between SQL and NoSQL, and specific versions of NoSQL you may hear about in the market. Lastly, I’ve included a graphic which presents use cases for both SQL and NoSQL by industry.

Structured, Semi-Structured, and Unstructured Data Types

Before we dive into the differences between SQL and NoSQL databases, it's important to understand the types of data that are typically stored in databases. There are three main types of data: structured, semi-structured, and unstructured.

Structured data is highly organized and can be easily processed by computers. Examples of structured data include customer information, transactional records, and inventory lists. This type of data is typically stored in a fixed format, such as tables or spreadsheets, and can be easily queried using tools like SQL.

Semi-structured data is information that doesn't fit neatly into a structured format but still has some identifiable structure. It may contain tags or labels that provide some context, but the content may vary in its format and organization. Examples of semi-structured data include email messages, social media posts, and web pages.

Unstructured data is information that has no identifiable structure or organization. It may come in the form of text, images, audio, or video, and it is not easily machine-readable. Examples of unstructured data include emails, documents, images, and video files.

While structured data is easy to analyze and process, unstructured data presents a challenge to businesses and organizations. The volume of unstructured data is growing rapidly, and these massive unstructured data stores require advanced technologies such as artificial intelligence and machine learning to extract insights and value from it. However, the insights gained from analyzing unstructured data can be highly valuable, providing businesses with a deeper understanding of their customers, operations, and markets.

Differences between SQL and NoSQL

SQL (Structured Query Language) databases, also known as relational databases, have been the standard for data storage for decades. They store data in a structured way, with rows and columns that can be easily queried using SQL. These databases were designed to handle structured data and provide strong consistency guarantees. They are still widely used for transactional systems, business intelligence and data warehousing. However, with the growth of big data and the Internet of Things, SQL databases are no longer able to handle the sheer volume and velocity of data being generated. This has led to the development of new types of databases, known as NoSQL databases, which are designed to handle large amounts of unstructured data.

NoSQL stands for "not only SQL," and as the name implies, it's a different way of organizing data that goes beyond the traditional tables and columns of SQL databases. NoSQL databases are designed to handle large amounts of unstructured or semi-structured data, such as social media posts, web pages, and sensor data. NoSQL databases are more flexible than SQL databases because they don't have a fixed schema or structure. This means that they can handle data that doesn't fit into neat rows and columns. NoSQL databases can store data in a variety of ways, including document-based, key-value, and graph databases.

Types of NoSQL Databases

One of the most popular NoSQL databases is Apache Cassandra. It is a distributed database designed to handle large amounts of data across many servers, providing high availability with no single point of failure. Think of it as a filing cabinet with many drawers and no locks, accessible to anyone who needs it. It is particularly well-suited for handling large-scale data that is spread across multiple data centers and cloud availability zones.

Cassandra utilizes a flexible data model that allows data to be stored in a denormalized way, which is particularly useful for handling wide and sparse data sets. This means that it can scale horizontally by adding more drawers to the filing cabinet as the volume of data grows. It is well suited for write-heavy use cases, where data is frequently added or updated.

Cassandra was initially developed at Facebook to handle the huge amount of data generated by the social network's inbox search feature. The project was started in 2008 and released as an open-source project in 2009. Cassandra was inspired by Amazon's Dynamo, which is a distributed key-value store, and Google's Bigtable, a distributed structured data store.

Cassandra's creators aimed to create a distributed database that could scale horizontally across many commodity servers and maintain high availability, even in the face of hardware failures. Cassandra's features, such as its decentralized architecture, ability to handle massive amounts of data, and fault tolerance, have made it a popular choice for many large-scale data-intensive applications, including social media platforms, e-commerce sites, and financial services.

Is Cassandra the Only NoSQL Database?

While Apache Cassandra is one of the most popular NoSQL databases, there are other types of NoSQL databases as well. Some other examples of NoSQL databases include:

• Document databases: these databases store data as documents, typically in formats such as JSON or XML. Examples include MongoDB and Couchbase and are often used for content management systems or e-commerce applications.
• Key-value stores: these databases store data as a key-value pair, similar to a dictionary or hash table. Examples include Redis and Amazon DynamoDB.
• Graph databases: these databases are designed to store and query data in a network of nodes and edges, making them well-suited for applications such as social networks or recommendation engines. Examples include Neo4j and Amazon Neptune.  Graph databases are well-suited for analyzing relationships between data points.

Use Cases for SQL and NoSQL

Conclusion

While SQL is still ideal for storing structured data, such as customer information or transactional records, the shift from SQL to NoSQL databases, like Cassandra, has allowed organizations to handle large amounts of data in ways that were not previously possible. NoSQL databases are now an essential part of modern data architectures, providing the scalability and performance needed to handle today's data demands. NoSQL databases have allowed businesses to store and process massive amounts of data more efficiently and cost-effectively, which has helped drive innovation and growth across a wide range of industries.