Spreadsheets work well for managing small datasets and handling straightforward tasks like organizing lists or running simple financial analyses. However, as data grows in size and interconnections—such as tracking relationships across multiple datasets or analyzing trends over time—spreadsheets quickly become unwieldy. This complexity often results in confusion, inefficiencies, and hours wasted trying to make sense of it all.

That’s where databases come in. They support tasks ranging from tracking financial performance to optimizing supply chains to predicting equipment failures. They don’t just store information — they turn data into a tool you can actually use. You can rely on databases to organize vast amounts of information, connect disparate datasets, and power features like automated reporting, real-time analytics, and streamlined workflows. Briefly put, they uncover patterns and insights that static spreadsheets simply can’t reveal.

Companies that make the most of their databases find better ways to work, using their data to solve problems and uncover opportunities. With the right setup, they can spot trends, make informed decisions, and run operations more smoothly. In this article, we’ll walk you through what databases are, how they work, and how they can transform the way you do business.

What is a database? 

A database is where businesses store, organize and link all kinds of data. It acts as a central hub for everything from structured information, like names and addresses, to unstructured content, such as images and videos. By organizing and linking data, databases spare you the headache of hunting down information and let you actually get work done.

Databases mainly come in two flavors: transactional and analytical. 

Transactional databases handle everyday operations like processing transactions and updating inventories — tasks where speed is paramount. They are the operational backbones of businesses, executing day-to-day activities efficiently. Examples include:

• MySQL: Open-source database for web applications and e-commerce platforms.
• PostgreSQL: Reliable database often used in financial systems and ERPs.
• Microsoft SQL Server: Integrates well with Windows-based business applications.
• Amazon Aurora: Cloud-native database for high-performance transactional workloads.
• Oracle Database: Secure, scalable solution for large enterprise transactional needs.

Analytical databases are the thinkers, handling complex queries that make strategic decisions. They handle large volumes of data to find trends or answer big questions, like determining buying patterns over time. The insights they provide allow companies to plan more effectively for the future. Examples include:

• Snowflake: A cloud-based data warehouse known for handling structured and semi-structured data.
• Google BigQuery: Created for high-speed analysis of massive datasets, ideal for real-time analytics.
• Amazon Redshift: A scalable data warehouse for analyzing data across multiple dimensions.
• Teradata: A powerful analytical database used for enterprise-scale data processing and insights.
• ClickHouse: An open-source columnar database optimized for high-speed analytical queries.

Whether it’s handling a customer's purchase or analyzing years of sales data to strategize for the future, databases help businesses run smoothly. They're indispensable tools, turning raw data into actionable strategies that boost revenue and enhance efficiency.

There are many types of databases, which we’ll get into later in this article. But for the time being, we’re going to primarily talk about relational databases, which are the most popular types of databases. You’ve probably heard of Microsoft SQL Server or MySQL, both of which are relational database management systems (RDBMS).

A quick survey of database components

Databases come in many forms, including relational database management systems (RDBMS) like MySQL and PostgreSQL, as well as NoSQL databases such as MongoDB and Cassandra. While the components outlined below are common to most database systems, specific implementations may vary depending on the database type. Understanding the structure of a database prepares you to manage and use it effectively. 

Here's a breakdown of the five key components that make up a typical database system:

• Hardware: All the physical tech that supports database operations, such as servers, computers, hard drives and network devices. You use these elements to host and run database software.
• Software: The core of database functionality, software allows you to create, manage and manipulate data. It includes the database management system (DBMS) which manages data access, the operating system and network software for data sharing.
• Data: At the heart of any database is the data itself, categorized into either structured data, semi-structured data or unstructured data. 
• Data access language: Primarily SQL (Structured Query Language),  the standard language for managing and querying relational databases. For non-relational databases, alternative query languages or APIs are used, depending on the database type.
• Procedures: The predefined rules that help users and administrators interact with the database system. Procedures help you manage data in a consistent and secure manner.

Recognizing how hardware, software, data types, SQL and procedures come together provides a solid foundation for advancing your database strategies and achieving robust data management.

What are databases used for?

Databases underpin a multitude of apps, from complex digital marketing tools to simple daily utilities. Here are some key strategies to effectively leverage digital marketing analytics and enhance your marketing campaigns.

• Streamlining real-time operations: Businesses often use operational databases to handle day-to-day transactions efficiently. They optimize these databases for speed, aiming to achieve real-time data processing and immediate responsiveness in fast-paced environments. 

Example: Pitney Bowes used Fivetran to achieve near real-time data visibility across 16 global distribution centers, enabling efficient tracking of over 800 million parcels per day.

• Boosting customer relationships: As your business grows, keeping track of every customer interaction becomes increasingly important. Customer relationship management (CRM) databases sort out all that customer info, making it easy to give customers a more personalized experience. Example: Raiffeisen Bank International leveraged Fivetran to provide real-time data access for over 300 employees, helping enhance customer acquisition and improve report quality.
• Powering modern software applications: Databases are essential for many software applications, from project management platforms like Trello or Asana to e-commerce websites and productivity tools. For instance, a project management app uses a database to track tasks, deadlines, and team member assignments, keeping everything organized and accessible.

Example: Square used Fivetran for seamless data centralization, enabling near real-time ingestion of various data types into their data warehouse to support their applications.

• Driving smart decisions with data: Advanced databases are the secret sauce for any business looking to get ahead. They analyze complex data to spot trends and patterns, empowering you to refine your strategies and make smarter choices, like ordering new inventory before it sells out using trend analysis from recent sales logs.

Example: JetBlue employed Fivetran to replicate aircraft maintenance data into Snowflake, allowing for proactive, data-driven maintenance decisions that prevent flight delays.

• Uniting data sources: Merging information from various sources into one place can transform your business outlook. Databases allow you to integrate everything, providing a bird’s-eye view of your operations and paving the way for insightful cross-functional analysis.

Example: Saks used Fivetran to onboard multiple data sources within hours, achieving near real-time data ingestion every five minutes, which enhanced their customer experience.

Each of these uses shows just how versatile and vital databases are in the digital age, helping businesses stay efficient and insightful.

The differences between relational vs. non-relational databases

Databases can be categorized into two types: relational and non-relational. Each handles data in unique ways, offering distinct data management strategies to suit different business requirements. Here’s how they differ. 

Relational databases

Relational databases, which commonly use SQL (Structured Query Language), organize and retrieve data neatly arranged in tables. Think of a relational database as a series of connected spreadsheets. Each table is filled with rows and columns, storing related bits of information. For instance, one table might hold all the customer details, while another could track their orders. You can think of it as a limitless spreadsheet with an unlimited number of tabs and links between the data.

To keep everything connected, relational databases use special identifiers called "primary keys" to link related information across tables. A primary key is unique — like a personal ID number for each row. In contrast, a "foreign key" acts as a bridge linking tables together, ensuring data from one table corresponds accurately with another. This setup allows for smooth and organized access to loads of related data, making it easier to manage complex relationships. Here are some examples of relational databases:

• Oracle database: Known for its robust performance and scalability, often used by large enterprises for mission-critical applications.
• MySQL: An open-source relational database commonly used for web applications and small to medium-sized projects.
• PostgreSQL: A powerful open-source option with advanced features like support for complex queries and large datasets.
• Microsoft SQL Server: A popular choice for businesses running on Windows, with strong integration capabilities for analytics and reporting.

Non-relational databases

In non-relational systems, data is often grouped into flexible schemas that can evolve over time, making them ideal for apps that require rapid development and scaling. For example, a document store might hold all customer interactions in one document per customer, avoiding the rigid structure of relational tables. They support faster access to unstructured data and can scale horizontally, making them ideal for handling large volumes of diverse data.

Unlike relational databases that use primary and foreign keys to maintain relationships, non-relational databases rely on embedded documents and linking through references. They simplify queries and operations by reducing the need to join tables. Examples of popular non-relational databases are:

• MongoDB: A document-based database for storing unstructured or semi-structured data, often used for web apps and content management.
• Cassandra: A wide-column store (stores data in a tabular format with flexible columns) designed for massive scalability and fault tolerance, frequently used in IoT applications and real-time analytics.
• Redis: An in-memory key-value store ((stores data as key-value pairs in memory for ultra-fast access)      ideal for caching, session management, and real-time applications like chat systems.
• DynamoDB: Amazon’s fully managed key-value and document database, built to handle high-scale workloads with low-latency performance.

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The impact of databases on modern business

Databases don’t just store data; they enable companies to react quickly to market changes. They’re indispensable tools for leveraging vast amounts of information to spur innovation across various sectors. Here are some areas where databases are transforming business operations:

• Supply chain management: Databases track everything from inventory levels to delivery statuses, ensuring that operations run smoothly and efficiently.
• Financial management: Databases keep financial records precise and compliance tight. Real-time financial tracking allows businesses to make informed decisions quickly, keeping them a step ahead in financial governance.
• Human resources management: Databases streamline everything from payroll to performance reviews. They allow HR teams to spend less time on paperwork and more time on what matters — supporting employees.
• Customer Relationship Management (CRM): CRM system databases let you have all your customer interactions and preferences at your fingertips. They help you personalize customer service and marketing like never before, turning data into deeper relationships.
• Business continuity and risk management: Databases give companies a clearer view of potential risks and how to handle disruptions. They help you make sure your operations run smoothly no matter what happens.

Businesses that harness databases significantly boost their agility and competitive edge. It gives them a tactical advantage to quickly adapt and seize opportunities the moment they arise.

Unlocking the power of databases through integration

As powerful as databases are, their true potential often lies in their ability to work together. Integrating data from multiple sources — whether it’s internal systems or external datasets — turns fragmented information into something meaningful and practical. That’s where data integration makes all the difference, helping businesses unify their data and make smarter decisions.

How data integration is evolving in database systems

The value of a database truly comes alive when you add a data integration layer. It’s what connects your databases seamlessly, giving you consistent, real-time access to the information you need most. Fivetran, a leader in this evolution, simplifies the integration process with robust data connectors that streamline the merging of data from diverse sources. They collect data and make it actionable in real-time, a necessity for businesses dealing with increasing data complexity.

Automation tools like Fivetran use AI and machine learning to revolutionize data integration. They actively spot and resolve data discrepancies, boosting both reliability and the effectiveness of decision-making processes. For example, algorithms can preemptively fix data errors to streamline integration, making it more efficient. A proactive approach lets businesses swiftly adjust to new tech and market shifts, turning extensive data sets into valuable insights with cutting-edge tools like Fivetran.

If you’re interested in seeing how this works, check out the Fivetran Connector Directory. Begin with a free trial or use the free plan.