In their relentless bid to become more data-driven, organizations around the world are reexamining every layer of their data stack, including databases that provide the foundation.
Choosing the right database is essential for leveraging data for multiple purposes, including business analytics, standard and ad hoc reporting, and visualizing data. At their core, they provide a reliable, understandable, and efficient way of storing and retrieving data. Databases can be classified into two main categories: relational and non-relational. Both types have their own unique advantages and disadvantages, ideal use cases, and associated costs. Choosing the right one, while challenging, is critical for success with any kind of analytics strategy. In this article, we will explore the differences between relational and non-relational databases and help you decide which one is best suited for your needs.
What is a Relational Database?
A relational database is a type of database that organizes data into tables with rows and columns. Each table represents a specific entity, and the columns contain the attributes or properties of that entity. The relationship between different tables is established using keys that link them together.
Advantages of Relational Databases
One of the major advantages of relational databases is their ability to maintain data integrity. They ensure that data is consistent and accurate by enforcing constraints such as primary keys, foreign keys, and unique constraints. This may happen through ELT or ETL methods, but by cleansing and integrating the data together, relational databases allow businesses to conduct workloads that require specificity and accuracy, such as business intelligence. Relational databases also provide a standard and well-defined structure for data storage and retrieval. This makes them ideal for applications that require complex queries and transactions.
Disadvantages of Relational Databases
The main disadvantages of relational databases is their rigidity. Depending on the platform you choose, relational databases can have a rigid structure that can make it difficult to accommodate changes to the data schema.
What is a Non-Relational Database?
A non-relational database, also known as a NoSQL database, is a type of database that does not use a table-based structure for storing data. Instead, they use a variety of data models, such as document-oriented, key-value, and graph-based.
Advantages of Non-Relational Databases
One of the major advantages of non-relational databases is their ability to handle large volumes of data and high traffic loads efficiently. They are designed to scale horizontally, which means that additional servers can be added to handle increased demand.
Disadvantages of Non-Relational Databases
One of the main disadvantages of non-relational databases is their lack of data consistency. They do not enforce constraints or rules on the data, which can lead to inconsistent data, incorrect insights, and bad decisions. Non-relational databases also lack the standard and well-defined structure of relational databases, which can make it difficult to perform complex queries and transactions, or make self-service analytics accurate enough for business users. . Additionally, some non-relational databases can be difficult to set up and maintain.
Key differences between Relational and Non-Relational Databases
Data structure
Relational databases enforce a rigid structure, such as a star or snowflake schema, that ensures data integrity and consistency. This makes them ideal for applications that require accuracy and a high degree of trust. In contrast, non-relational databases offer more flexibility in data structure, making them ideal for applications that require storing unstructured data or handling high-velocity data streams.
Scalability
Relational databases are vertically scalable, which means that they can handle increasing loads by adding more resources to a single server. Non-relational databases are horizontally scalable, which means that they can handle increasing loads by adding more servers to a cluster. This makes non-relational databases more suitable for applications that need to handle massive amounts of data or require high availability.
Flexibility
Relational databases are less flexible than non-relational databases when it comes to handling changes in data structure. Changing the schema of a relational database requires significant effort and can lead to data loss. In contrast, non-relational databases can easily handle changes to the data structure without the need for downtime or data loss.
Querying
Relational databases offer a powerful query language, such as SQL, which makes it easy to perform complex queries and joins between multiple tables. Modern business intelligence platforms like ThoughtSpot provide a simple, intuitive way to translate natural language into SQL to extract insights. Non-relational databases, on the other hand, have limited query capabilities, relying on data science techniques. Querying data can be more challenging.
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Data relationships
Because relational databases are based on the relational model, they are particularly well-suited for managing data relationships, including complex data where many different attributes overlap. Relationships between data can be defined using primary keys and foreign keys, which ensure data integrity and help prevent data duplication. Non-relational databases, on the other hand, are not as well-suited for managing complex relationships between data. While some non-relational databases do support relationships, they tend to be less rigid and more flexible than the relationships defined in a relational database.
Data storage
Another key difference between relational and non-relational databases is how they store data. Relational databases store data in tables, which are organized into columns and rows, which helps make the data immediately useful for analytics and applications. Each row represents a single record, and each column represents a single attribute of that record. Non-relational databases, on the other hand, can store data in a variety of ways. For example, document-oriented databases store data as documents, key-value stores store data as key-value pairs, and graph databases store data as nodes and edges.
When to use a Relational Database?
Relational databases are ideal for applications that require data consistency, structured data, and complex relationships between data entities. For example, a banking application that needs to store customer data, transaction records, and account information would benefit from using a relational database.
When to use a Non-Relational Database?
Non-relational databases are ideal for applications that require scalability, flexibility, and handling unstructured data. For example, a social media platform that needs to store user-generated content, such as photos, videos, and posts, would benefit from using a non-relational database.
Move beyond traditional methods for better insights
All in all, it’s clear that relational and non-relational databases present distinct opportunities for data storage. You should carefully consider both approaches to determine which might be most appropriate given their specific use cases and needs. Considering the benefits of relational and non-relational databases can help you make the right decision when deciding which one to use for projects.
If you are looking to solve challenges that require a high degree of confidence in your data’s accuracy and integrity, a relational database may be the right choice for you. Getting the most value out of this type of database, however, requires empowering business users who need to engage with the data stored. That’s where tools like ThoughtSpot come in. With ThoughtSpot, you can immediately connect to any cloud data platform and democratize this data across your organization through the ease and familiarity of search. If you’re ready to make the most of your data and investments in a relational database, sign up for a ThoughtSpot free trial today.
