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Six Types of Databases With Examples

Databases are a common fixture in business today, helping organizations store data in a structured and organized manner and providing a reliable data source for building OLAP and OLTP systems and populating other data storage systems. Databases are sources for organizational data warehouses, which store historical and current business data.

Various databases exist, each catering to different business needs. They differ in storage methods, query languages, and business use cases. Popular databases include relational (SQL), non-relational (NoSQL), graph, open source databases, and many others. Let’s explore these database types in detail and how each differs in usage and benefits to businesses today.

Understanding Different Types of Database

Businesses use multiple databases today, each with strengths and weaknesses and designed to cater to special business needs. For example, businesses handling highly unstructured data may benefit from the flexible schema offered by a NoSQL database.

Relational Database

One of the most common databases companies use today, relational databases use SQL programming and organize their data in highly structured tables as rows and columns, allowing for multiple types of relationships between tables. Another reason relational databases are a popular choice for businesses today is the ACID (Atomicity, Consistency, Isolation, and Durability)-compliant nature of its transactions, which is critical to maintaining the accuracy and reliability of business operations.

As an example illustrating the value of ACID-compliant database transactions, consider a banking system using a relational database for maintaining its customer transactions:

Atomicity ensures an all-or-nothing effect on transactions. This means that all parts of a transaction must be completed successfully for it to be considered valid. If an error occurs at any point during the transaction, all changes made must be rolled back to the database’s original state.
Consistency is achieved with atomicity and ensures the database remains consistent after completing transactions. Therefore, a transfer from a user’s account means debiting a customer and crediting another, leaving the total value of both customers the same as before the transaction.
Isolation ensures transactions proceed independently of one another. For example, multiple transactions co-occurring must have individuality and should not affect the other.
Durability helps persist the state of the database after a transaction proceeds, even in the event of a system failure. For example, successful transfers made before a system crash must persist after restoring the system.

Relational databases can handle many concurrent user queries and updates with fast response times, which makes them a good choice for building OLTP applications and Operational Data Stores (ODS) that help drive day-to-day business operations.

Non-Relational (NoSQL)

Unlike relational databases, NoSQL databases do not store data in tables or use SQL as its querying language. Non-relational databases store unstructured data in various ways, depending on the business use case. Some of these methods include:

Documents as seen in MongoDB, which stores its data as a collection of documents in JSON or XML formats. Document databases have the ability to handle semi-structured or unstructured data, making them suitable for evolving data schemes.
Key-value databases store data in key-value pairs that store attributes as keys and their value. It is the simplest NoSQL database that requires no querying language and is easy to design and implement. Businesses may employ key-value databases for storing user information like customer profiles and cart information or for applications that don’t require complex queries or frequent updates. These databases can also handle a high volume of reads and writes from concurrent users, making it an excellent choice for handling session management for storing and retrieving user authentication tokens and preferences.
Column-oriented databases or wide-column stores use keyspaces to group data in column families, with every family possessing a unique key. Although referred to as column-oriented databases, these databases can also exist as rows. Column-oriented databases are great for business analytical processing in data warehouses as the column design allows for faster query performance. Additionally, arranging column values of the same type will enable data compression, reducing disk storage requirements.
Graph databases excel at representing complex relationships between data entities, making them ideal for applications like social networks, recommendation engines, and fraud detection.

Managed Versus On-Prem

Managed databases employ a database provider for their database services. Unlike the traditional on-premise database option, managed databases come with no upfront hardware or software acquisition cost, enabling easy implementation as businesses only need to specify their storage needs to get started. Examples include Snowflake, MongoDB Atlas, and Azure SQL Database for MySQL Heatwave.

On the other hand, with on-prem databases the organization retains complete control of its infrastructure for an on-premise database and uses a team of database administrators to set up, manage, and maintain it. These databases come with an expensive upfront cost of hardware and software and a team of employees with specialized skills to perform regular updates or patches to ensure optimal database performance.

Closed Versus Open Source

Open-source databases are databases where the source code is available to the general public for download, modification, and distribution, meaning organizations can tailor these databases to fit their specific needs. Examples include SQLite, PostgreSQL, MongoDB, and MariaDB.

For closed databases, the source code is private and cannot be accessed or modified by members of the public. Some differences between open-source and closed databases include the following:

Cost: Gitnix reports a 33% reduction in organizational cost from using open-source software. No software and hardware acquisition cost means reduced costs for open-source databases. In contrast, closed databases require upfront payment to acquire licenses that often require renewals periodically, which attracts more costs.
Speed of updates/patches: The availability of open-source code to the general public means more eyes and visibility on the source code; hence any arising issues or bugs found are resolved quickly by community members. For closed databases, updates/patches follow a corporate procedure, which may mean a longer period to resolution.
Community/networking opportunities: Open source databases create a robust ecosystem that provides potential networking opportunities for individuals to grow their skills via participation and/or mentoring. The closed nature of proprietary databases limits the forming of community relationships.

Other Types of Database Models

Other types of database models used today include:

Object-oriented databases follow the object-oriented programming language and represent their data as objects. It employs object programming concepts like polymorphism, encapsulation, inheritance, and abstraction on its objects. Companies built on OOD languages like Kotlin, Java, and Swift use OOD for working with complex data structures.
Flat databases: This database stores data as flat files in a single table and separates these records using delimiters like commas. Unlike relational databases that store their data as tables with relationships existing between them, flat databases have no relationships between their records. They help perform simple database operations like spreadsheet applications or computer programs for saving configuration information.
Network databases resemble hierarchical databases with a root-like structure, but the child nodes can have more than one parent node. Network databases allow for multiple, flexible relationships between entities and can help build customer databases.
Hierarchical databases have a tree-like structure like network databases, but child records can only have single parents; this means many-to-many relationships are impossible. This database is inflexible and requires multiple pointers for operations, which slows performance.

Elevate Your Database With StreamSets

StreamSets is a powerful data integration platform that can significantly accelerate a wide range of use cases while offering unmatched flexibility in handling diverse data sources and formats. Whether you’re dealing with structured or unstructured data, StreamSets provides a seamless solution. StreamSets Data Collector simplifies the process of replicating entire datasets from various sources, including relational databases like SQL Server and Oracle, as well as non-relational databases like MongoDB. Leveraging the Java Database Connectivity (JDBC) Multitable Consumer, StreamSets enables efficient data consolidation efforts. This capability ensures that organizations can effortlessly sync data between their source systems and data warehouses, enabling real-time or batch-driven data integration strategies, depending on their specific needs.

Aside from data ingestion, StreamSets also empowers users to build data transformations to enable quicker time to insights. With native Snowflake integration, users can create, deploy, and manage pipelines that execute directly in their data cloud. There’s no need to be a SnowSQL expert to harness the power of StreamSets. The drag-and-drop technology lowers the barrier of entry for transforming data inside the Snowflake ecosystem.

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