One thing every business has in common? Piles and piles of data.
For most of those businesses, that means lots of energy spent on storing, organizing, and figuring out how to effectively use those vast quantities of data. A recent study found that extracting better insights and making more strategic decisions using data was one of the top motivations for using AI, with 27% of enterprise businesses ranking it first in their priorities.
The most common tools used to manage data are spreadsheets and databases. While both serve many of the same purposes, they are they are fundamentally different in structure and capabilities.
Many business users and startup teams rely on Excel spreadsheets or Google Sheets for tasks like budgeting, tracking projects, or analyzing data. As data grows more complex, however, having a relational database (such as MySQL or Oracle) often becomes essential. Understanding the differences between a database vs spreadsheet is important for anyone interacting with business data, no matter how technical.
In this article, we’ll compare spreadsheets and databases in terms of structure, functionality, use cases, and more – and explain why it matters which one you use.
What is a Spreadsheet?
A spreadsheet is a digital document organized into a grid of rows and columns, known as a tabular format. Each cell can contain data (text, numbers, dates, etc.) or formulas that perform calculations on other cells. Microsoft Excel and Google Sheets are classic examples of spreadsheet applications. An Excel workbook (Excel file) can have multiple sheets or tabs, essentially multiple tables of data in one file.
Spreadsheets were originally designed for accounting and simple calculations, and they are great for tasks like financial modeling, list tracking, and creating charts and graphs (for example, with Excel’s pivot tables and pivot charts for summarizing and visualizing data). Spreadsheets are very user-friendly, allowing quick data entry and manipulating data by dragging cells or writing formulas. For a single user or small dataset, an Excel spreadsheet or similar is often the go-to tool due to its simplicity and flexibility.
What is a Database?
A database is a more complex system for organizing and storing data, typically managed by a database management system (DBMS) or database program. Data in a relational database is stored in one or more tables, where each table consists of rows (records) and columns (fields). Unlike the free-form cells in a spreadsheet, each column in a database table is defined with a specific data type (e.g. integer, text, date), and each row must adhere to that schema. This structured approach is part of data modeling – designing a schema that organizes data into related tables.
A relational database is a specific kind of database that uses a structured format of tables and establishes relationships between those tables, allowing the data to be easily connected and cross-referenced when needed. Common relational databases (also called RDBMS for Relational Database Management System) include MySQL, PostgreSQL, Microsoft SQL Server, and Oracle Database. Even Microsoft Access is a desktop database that uses the Jet engine to store tables and can be considered an “Access database” for smaller-scale needs.
Databases are accessed using queries, which are usually written in SQL (Structured Query Language) rather than by manually interacting with cells. Because of their design, databases can enforce data integrity rules, allow multiple users to access data simultaneously, and handle large volumes of information efficiently.
Need to learn how to use SQL?
Read this guideKey differences between spreadsheets and databases
While spreadsheets and databases are mostly very similar, they have important differences that make one or the other better for different business use cases. There are also hybrid tools, like Airtable or Google Sheets with scripting, that blur the lines, but fundamentally understanding spreadsheets vs databases helps in adopting those as well. Let’s take a closer look.
Appearance & data visualization
Spreadsheets often include formatting and presentation as part of the document (colors, cell highlights, merged cells, etc.). Most spreadsheet platforms also have built-in options for charts, graphs, and other data visualization. You may even be able to embed images, graphs, charts, and other elements into spreadsheet cells.
Databases separate data from presentation. In a database, you cannot set a font color or cell shading in the table storage – the focus is purely on data. Any formatting or visualization is done by external tools or applications. Databases don’t natively provide charting or visual formatting, so you would typically use a separate tool (like a BI application or export to a spreadsheet) for graphing results. This makes databases ideal for back-end data storage and spreadsheets useful as a front-end for displaying or tweaking data on a small scale.
Ease of use
Spreadsheets have a very low barrier to entry. Pretty much anyone with basic computer literacy can jump into a spreadsheet and add some formatting. While the basics are easy to use, you can absolutely have technically skilled power users who can do complex formatting and use advanced Excel or Google Sheets formulas to create really sophisticated spreadsheets.
In contrast, databases require technical skill to use even at a basic level. Unlike a flat spreadsheet, databases often require using SQL queries (as seen in the query editor) to retrieve and manipulate data, rather than editing data directly cell-by-cell. You may need to create a SQL GUI to let non-technical users access the data stored in a database without learning queries.
Structure: rows & columns vs. relational tables
In a spreadsheet, any cell can contain any type of data or even a formula. There is nothing preventing you from putting a phone number in a “Name” column or mixing text and numbers in the same column. A spreadsheet is essentially a two-dimensional grid for data entry and calculations.
In contrast, a database has a defined schema. Each table requires predefined columns with set data types (e.g. an “Age” column might be defined as an integer type, a “Date” column as a date type). If you try to enter the wrong type of data into a field, the database will reject it to maintain consistency. This means a database provides a more organized and controlled format for data storage.
Spreadsheets offer no true concept of relationships between separate sheets except through manual linking (like using lookup formulas). Databases, on the other hand, can define relationships between tables (hence relational database): for example, a “Customers” table and an “Orders” table can be linked by a Customer ID. This relational structure prevents duplication and maintains consistency across large datasets.
Functionality: Calculations, queries, and data manipulation
Spreadsheets and databases also differ in how you manipulate data and perform calculations or analyses.
In a spreadsheet, you can write formulas directly in cells (e.g., =SUM(A2:A100)), create pivot tables for summarizing data, and use built-in functions to perform complex calculations. They are excellent for visualization and charting. Spreadsheets also allow for scripting and automation through tools like Excel VBA (Visual Basic for Applications) macros, which can automate repetitive tasks in an Excel file. This makes spreadsheets very powerful for interactive analysis by a single user.
Databases approach data manipulation differently. You use queries (usually SQL queries) to filter, aggregate, and combine data. For instance, to get the total sales by region from a database, you might write an SQL query with a SUM() aggregation, whereas in a spreadsheet, you might use a pivot table or a SUMIF formula. SQL queries are extremely powerful and can join data across multiple tables, something that is very hard to do in a spreadsheet.
Importantly, a query does not alter the data, it just analyzes it. In a spreadsheet, it’s easy to accidentally change a value or formula while analyzing data, because the interface is the data. In databases, data is accessed and analyzed through queries without altering the underlying records, preserving data integrity.
Stability: Data integrity and validation
Databases are excellent at maintaining data integrity because of their structured schema. They enforce rules like unique keys, required fields, and referential integrity (e.g., you cannot delete a record that is referenced in another table). A database can ensure, for instance, that an email address isn’t entered twice for a field that should be unique, or that a foreign key reference actually exists. In a database, updates follow ACID properties (Atomicity, Consistency, Isolation, Durability) in a transactional manner, making sure the data stays reliable. This high level of integrity is important for business-critical data.
In a spreadsheet, you maintain data hygiene manually. You could use data validation rules or careful input controls in Excel or Google Sheets, but these can be bypassed or incorrectly set fairly easily. Because of this, spreadsheets are more prone to errors like duplicate entries, inconsistent values, or broken formulas. For example, if two people are updating a customer list in a spreadsheet, it’s easy to accidentally have contradictory or missing information.
Scalability and performance
One of the biggest reasons organizations move from spreadsheets to databases is scalability. For large volumes of data, databases significantly outperform spreadsheets.
Spreadsheets have practical limits. Excel, for example, caps each sheet at 1,048,576 rows and 16,384 columns. Google Sheets allows up to 5 million cells in total. A spreadsheet becomes sluggish and difficult to use as it begins to overload. Handling tens of thousands of rows with complex formulas can degrade performance in Excel significantly. Spreadsheets are also typically loaded into memory on your PC or in your browser, which means very large files can crash or become unresponsive.
Databases are designed for large-scale data and high performance. A relational database can easily handle millions of records and execute complex SQL queries on them efficiently. The data is indexed and stored on a server that is optimized for querying. As the data grows, you can scale up the hardware or optimize the database (through indexing, query optimization, etc.) to keep performance high. You could have a table with 10 million transactions in MySQL and still retrieve summary reports in seconds or minutes, which would be impossible to even open in Excel.
Another part of scalability is multi-user access. When working as a team, multiple people often need to work with your data. Spreadsheets aren’t great for simultaneous collaboration. Cloud-based spreadsheets like Google Sheets or Excel Online allow multiple users to edit at once. However, you risk accidental overwrites, and it’s hard to maintain a single source of truth. Users often end up with multiple copies of an Excel file (“Report_final_v2.xlsx”, etc.), making version control challenging.
Databases, by contrast, are built for concurrent access. Many users and applications can access the same database at the same time, with fine-grained access control and user permissions. A database can handle transactions from many users, and changes are saved in a centralized way. DBMS systems even track changes with logs, so you can audit who changed what. This makes databases a better choice for enterprise-level data management, where data consistency and collaboration are key. Databases can also be better in terms of reliability and backup, since most enterprise databases have automatic backup, recovery, and failover support.
Choosing the right tool for your use case
Most businesses will use a combination of spreadsheets and databases for different purposes. Typically, businesses lean harder on databases as they scale and their quantities of data scale, too. Sometimes, they’re even used together. A common practice is to store data in a database for integrity and performance, then export or connect to a spreadsheet for analysis or presentation (for example, pulling a report from MySQL into Excel for creating a graph).
Understanding the strengths and limitations of each can help you choose the right tool for a given job.
When to use a spreadsheet
Use spreadsheets for individual or small team tasks with limited data. Spreadsheets are quick to set up and easy to use, making them an important tool for organizing smaller quantities of data for active tasks. The learning curve is minimal, so most people can start entering data and using basic formulas without training.
Spreadsheets also make sense when the data is one-off or not part of an ongoing integrated application. They’re perfect for doing a what-if analysis on marketing spend in an Excel spreadsheet or maintaining a personal to-do list. Spreadsheets are also useful as a data modeling sandbox. For example, quickly plotting data in a graph or using a pivot table to explore data trends.
Common use cases for spreadsheets include financial planning, small inventory lists, or simple project trackers. They’re also an important tool when you need to do quick data analysis with charts or you need a one-off calculation and visualization.
When to use a database
If your data is large and growing, needs multi-user access, or requires strict data integrity, a database is usually the better choice. Use a database when you need scalability, concurrency, and structured storage, and plan to manage data over the long term as an asset. In any situation where multiple types of data need to be linked (customers, orders, products, etc.), you’ll need a relational database.
Databases are best for tasks that involve repetitive queries, reporting, and analyzing data across large datasets. Businesses often start with spreadsheets and then migrate to a database as the information becomes too complex or voluminous to manage in Excel. For instance, a sales team might outgrow an Excel file of leads and move to a CRM system, which is essentially a specialized database application.
Another sign you need a database is when you require access control, such as different permissions (read/write) for different users, or when you need to integrate data with other systems (since databases can be queried by other software easily). This makes databases a great tool for powering applications. For example, the back end of a web application or an internal tool will use a database to store and retrieve data.
When to build an interface for your business data
Speaking of apps, sometimes you need to use business data in ways that aren’t possible or practical with either a database or a spreadsheet. In these cases, you might need to connect an interface to that data. You can use a no code tool like Glide to create a custom app as an interface for either kind of data source.
Control visibility or share data outside your org
An app interface makes it possible to receive data without making your data visible. It’s ideal when you need to collect private data or allow users outside your team, such as clients or customers, to interact with your data. Though you can set user permissions and visibility controls in a database or spreadsheet, it’s more secure to just lock that data fully behind an interface. You’ll also be less likely to have accidental interference with your data.
Allow non-technical users to interact with databases
If you have non-technical team members who need to interact with your data but don’t understand SQL queries, it can be really challenging to unlock that data for them. A SQL GUI (graphical user interface) makes it possible for anyone to easily access and even manipulate that data without having to understand databases on a technical level. Interfaces helps break down data silos and democratize all the data across your organization.
Visualize or display data
While some spreadsheets have visualizations you can create, they’re fairly basic. And databases don’t have any options for displaying data. You can create an interface on top of that data that gives you much more creative control and freedom for displaying that data. It’s much easier to manage inventory using a gallery display with product specs and fields than trying to scroll through a spreadsheet. If you need to analyze your data or display it for leadership reports, having a data dashboard with clean charts, filterable displays, and live graphs is a great help. Apps are a more intuitive and visually appealing way to interact with data than just trying to manage with a raw spreadsheet.
Extend your data with AI or automation
Data at work isn’t typically static. It’s always being used to drive actual work. When you need to actively use your data, building an interface gives you more flexibility to extend your data with other forms of tech like APIs that connect it to other software, workflows that update data on a schedule, or intelligent automation to help your team use AI on your data.
Learn how to build a data dashboard
Without any codeHow understanding data makes teams better
Using data well is deeply important for every business. Your data does everything from powering daily tasks to driving strategic decisions for your entire org.
Choosing between a spreadsheet and a database is not just a technical decision but also a strategic one for data management. Using the wrong tool can lead to data integrity issues, scalability problems, or inefficiencies. If you find yourself wrestling with huge Excel files, worrying about version control, or unable to enforce data consistency, it’s a sign that a database solution is needed.
If you find that your teams are hitting walls and finding work inefficient using these tools, it’s time to create apps for them to use that data better. You can build your own using no code, even if you don’t have engineering resources on board.
Ultimately, having all three of these tools in your back pocket will make your business more successful.
