What is a Database?

What is a database?

A database is a collection of information that is stored digitally in a computer, on a server, or in the cloud. Databases can be very simple or quite complicated, depending on the structure and organization of the data. The database system is typically composed of the information itself and a database management system (DBMS), allowing users to easily access, update, analyze, and manage the information. 

There are many types of databases. The most common—and traditional—type is the relational database, which organizes data into relational tables made up of rows and columns. The data can include lists of customers with contact information, inventory records, sales records, financial information, and much more. Relational databases are used to support many types of applications, including eCommerce, inventory tracking, customer relationship management applications, and many more.

SQL and NoSQL databases

Virtually all relational databases use structured query language (SQL) to add, update, query, and delete data stored in a relational database. SQL is a programming language originally developed in the 1970s by IBM. It is the language used by relational databases when users want to create or change databases, manipulate data, retrieve specific information, and run various reports. 

Other types of databases include key-value, document, wide-column, and graph databases - collectively known as non-relational or NoSQL databases. Non-relational databases differ from relational databases in that they organize data in a non-tabular format and use methods other than SQL to manipulate data, which makes them conducive to a wide variety of use cases. There are drawbacks to non-relational databases, however, including that some lack support for ACID transactions. 

Database evolution

Databases have evolved significantly over the decades to meet the ever-growing demands of businesses and technology. Early databases were simple, hierarchical structures designed for mainframes, but as data needs expanded, relational databases emerged, offering structured storage and SQL-based querying. The rise of the internet and big data brought NoSQL databases, enabling flexibility and scalability for unstructured and semi-structured data. Today, cloud-native and distributed databases power modern applications, leveraging AI, automation, and real-time analytics. As businesses continue to generate massive volumes of data, databases will keep evolving to support new workloads, from IoT to AI-driven decision-making.

How databases work

Databases allow users to enter information in various ways, either structured or unstructured. Then, through a software program, users can manipulate the data as desired, forming relationships between pieces of information. 

All databases require a DBMS, as mentioned previously. The DBMS is the “brain” of the database and the component that serves as the bridge between the data itself and users. These systems make it possible for users to manipulate data without having to know precisely where and how the data is stored. Instead, the system handles identifying and accessing data independently. DBMSes not only allow users to add, delete, change, update, and organize information, but also come with a range of administrative functions, including backup and recovery, and performance monitoring and optimization. 

According to DB-Engines’ January 2025 ranking, the top 10 most popular DBMSes include:

• Oracle
• MySQL
• Microsoft SQL Server
• PostgreSQL
• MongoDB
• Snowflake
• Redis
• IBM Db2
• Elasticsearch
• SQLite

What’s the difference between a database and a spreadsheet?

Like databases, spreadsheets are often used to store and organize data. But they’re not the same things. Spreadsheets, such as are commonly used in Microsoft Excel, are much simpler than databases and are typically meant for an individual user or maybe a few users as opposed to tens, hundreds, or thousands or more users. 

The biggest differences between databases and spreadsheets are:

1. How data is stored, accessed, and manipulated,
2. How much information can be stored, 
3. Who can access that information. 

Databases can store massive volumes of data that would be unwieldy in a simple spreadsheet, and many users can access and manipulate the data in databases. Users can also query the database to find specific information using more complex and comprehensive parameters than is possible with spreadsheets. 

Components of databases

Database structures can vary widely, but they typically include the following five main components:

• Hardware - the physical computers, storage systems, and devices on which the database software runs.

• Software – the DBMS, or system that gives users control over and access to the data, which usually has a user-friendly interface and control panel.

• Data – the actual information stored in the database, which is organized for easier access and management.

• Data access language – a programming language, such as SQL, that is used by the DBMS to manage and organize the data. Users also need this language to write commands and query the database.

• Procedures – defined rules that users follow to access data and organize it via the DBMS.

What is a database used for?

Businesses use databases to store, organize, access, and manage important information, from patient health records at a large metropolitan hospital to sales records at the corner florists’ shop to social media usage statistics and patterns at a global communications provider. With that data, businesses can:  

Manage data for transactional applications

Virtually all databases are used to manage data associated with applications. Conversely, any application that uses data requires a database. For example, an eCommerce website that enables customers to purchase products and services requires a database to make a record of each transaction. A customer relationship management application requires a database to organize data associated with each customer and to track and store any changes made to the data. Broadly speaking, these types of applications are referred to as transactional applications. Data associated with transactional applications is often added, updated, and deleted frequently. This requires a database that supports both frequent reads (the process of viewing data) and writes (the act of changing or manipulating data). 

Analysis of data

In addition to transactional applications, databases also support analytical applications. Analytical applications make it easy for users to view trends hidden in data and uncover insights that could benefit the business. These types of applications require databases with the ability to read and query large volumes of data  By analyzing data in databases, leaders can make more informed—and therefore smarter—decisions for the company’s future. Using today’s advanced data analytics platforms, they can delve deep into their data and get actionable information they wouldn’t have been able to recognize themselves. AI and machine learning are transforming data analytics and giving organizations a competitive edge in their industries. For example, by keeping data on sales, inventory, customers, and more, businesses can identify patterns and detect factors that can help them improve business processes, customer experiences, and cost-effectiveness. 

Data security

Databases store vast amounts of sensitive and valuable information, making security a top priority. Businesses must implement robust security measures to protect data from breaches, unauthorized access, and cyber threats. This includes encryption, access controls, authentication mechanisms, and regular security audits. Compliance with industry regulations, such as GDPR or HIPAA, is also essential for businesses handling customer or healthcare data. A well-secured database ensures data integrity, confidentiality, and availability, safeguarding both the business and its customers.

Scaling 

As businesses grow, so does the volume of data they generate and process. Databases must be able to scale efficiently to accommodate increasing workloads, whether by adding more storage, improving query performance, or distributing data across multiple servers. Horizontal scaling (adding more database instances) and vertical scaling (enhancing the power of a single instance) are common approaches to handling larger datasets. Cloud-based and distributed databases offer flexible scaling options, enabling businesses to meet demand without compromising performance.

Types of databases

While all databases serve the same fundamental purpose, they differ in structure, scalability, and use cases. Below are some of the most common types of databases and their key characteristics.

Hierarchical database 

Structured like a tree, hierarchical databases store data in a parent-child relationship, ensuring fast and predictable access patterns. They are commonly used in applications like directory services and mainframe systems, where data follows a clear hierarchy.

Network database 

A network database expands on hierarchical structures by allowing multiple relationships between records, forming a flexible graph-like model. This type of database is particularly useful for complex relationships, such as supply chain management and telecommunications networks.

Relational database 

Relational databases organize data into structured tables with predefined relationships, making them ideal for transactional applications. They use SQL for querying and ensure data consistency through ACID compliance. Popular examples include MySQL, PostgreSQL, and Microsoft SQL Server.

Object-oriented database 

Designed for applications that use object-oriented programming, object-oriented databases store data as objects rather than traditional tables. This makes them well-suited for use cases involving multimedia, complex data structures, and real-time analytics.

NoSQL database

NoSQL databases break away from rigid schemas to support unstructured and semi-structured data, enabling high scalability and flexibility. They come in various forms, including key-value, document, column-family, and graph databases, making them ideal for big data, IoT, and modern cloud-native applications.

Challenges of databases

As organizations continue to deal with ever-increasing volumes of data, today’s databases are becoming more massive and more complex than ever. This brings increased challenges, which often include: 

• Handling enormous—and growing—data volumes and user demand. The sheer size of databases alone makes managing and organizing that data more complicated. The underlying demand is for fast, efficient access to specific data whenever it’s needed, and that can be daunting for database administrators to keep up with. 
• Making databases easily accessible to developers. As mentioned earlier, virtually all applications require a database to manage the data associated with it. When a software developer builds a new application or a new feature for an existing application, they will often need to deploy and run a database to support it. Making sure developers have fast, easy access to databases to support rapid, agile development is a challenging but crucial task for DBAs and operations teams.
• Adequately protecting that data. The number of data breaches each year continues to grow, and ransomware is rampant. Organizations need to keep their databases well protected—especially because that raw data is often the target of malicious actors. Patient data, sales numbers, earnings, product specifications, and personnel records are all very attractive and lucrative “gets” for hackers. 
• Ensuring consistent access to and performance of the database. Regardless of how big they get, an organization’s databases must be maintained and updated regularly. Patching and updating multiple large databases can be challenging, but the organization’s success is riding on the ability to maintain consistently high performance and availability as users and applications access database information. 
• Seamlessly scaling as the business grows. As an organization grows, it must be able to scale databases accordingly. More users need access from more remote locations, and more data is pouring into the system as the business expands. It can be difficult for database admins and operations teams to prepare for growth and predict the database capacity the business will need in several months’ or a year’s time. But having that capacity when it’s needed is critical to smooth and sustainable growth. 
• Maintaining data privacy, residency, and sovereignty. As governments and industries increase regulation around privacy, data residency, and data sovereignty, things can get complicated for database administrators. It’s not always clear which regulations apply to which volumes of data, but making mistakes in this area comes with big penalties and a lot of hassle.
• Making sure all sources of data can be integrated and efficiently analyzed. Now that data is pouring into organizations from many different sources, from IoT systems and surveillance cameras to online purchasing and CRM systems, it’s important that they have a way to integrate all of this disparate data together. Many businesses rely on data lakes and data warehouses to store information, which can allow users to still use a single database interface to access and manage information.

The future of databases

• AI and Machine Learning integration - Databases are increasingly incorporating AI and machine learning to automate tasks such as data optimization and predictive analytics, enhancing efficiency and decision-making. 

• Cloud-native and serverless architectures - The adoption of cloud-native and serverless database solutions is on the rise, offering businesses scalable, cost-effective, and easily manageable data storage options. 

• Real-time data processing - The demand for immediate insights is driving the development of databases capable of real-time data processing, enabling businesses to respond swiftly to emerging trends and information. 

• Enhanced data security and privacy - As data breaches become more sophisticated, future databases are expected to integrate advanced security measures, including robust encryption and compliance automation, to protect sensitive information.

The good news is technologies such as hyperconverged infrastructure and Database-as-a-Service can help organizations overcome these and other database-related challenges so they can better serve customers and get the most value from their data.

How Nutanix can help overcome database challenges

Nutanix helps its customers better run and manage databases in two key ways.

First, Nutanix Cloud Platform (NCP) is the perfect solution for business-critical applications and databases in hybrid multicloud environments. It provides consistent performance that satisfies end users, enhanced availability, stronger full-stack security, and enables greater agility to meet dynamic business needs — all while reducing operating costs and management complexity. NCP is the only solution of its kind that allows you to deploy legacy and modern business apps and databases anywhere you want — on-premises, in the cloud and at the edge. 

Second, Nutanix can significantly simplify database management and help accelerate software development with Nutanix Database Service (NDB). NDB is the only Database-as-a-Service that simplifies and automates database lifecycle management across on-premises and public clouds for:

• Microsoft SQL Server,
• Oracle Database,
• PostgreSQL,
• MySQL, 
• MongoDB.

It enables DBAs and platform teams to efficiently and securely manage hundreds to thousands of databases to meet performance, scalability, security, high availability, disaster recovery, and cost requirements without sacrificing control or flexibility. And, it makes database provisioning simple, fast, and secure for developers in support of agile application development.