Access 101: What is a Table in Access?

If you know Excel worksheets, Access tables will look very familiar. Be careful, though! There is a "key" difference between the two.

Access 101: What is a Table in Access?

This article is part of my Quick Start Guide to Microsoft Access series, Access 101.

A table in Microsoft Access is a data storage structure that organizes data in a structured format, similar to a spreadsheet such as Microsoft Excel. Each table typically represents a specific object or relationship, such as customers, orders, or products.

A table in Access is made up of columns and rows.

Columns (Fields): Each column in a table represents a different field. A field represents some attribute of the entity being recorded. For instance, in a "Customer" table, you might have fields for a unique customer ID, the customer's first name, last name, etc. Each field has a specific data type such as text, number, date/time, etc.

Rows (Records): Each row in the table represents a record. A record is an individual instance of the object the table represents. For example, in a "Customer" table, each row would represent a single customer.

Here is a simple example of what a table might look like in Access:


In Access, tables can be linked together to form relationships, which allows for complex data organization and reporting. This is a key feature of Access, and what makes it a powerful tool for managing data. This kind of structure is known as a relational database.

The Key Difference Between Excel and Access

Many people who are new to Access have a strong background in Excel.  Moving from Excel to Access can require a significant shift in thinking, though.

In Excel, you're used to working with a single worksheet or multiple worksheets that are relatively independent from each other. Each worksheet might contain a list of items, with each row representing an item and each column representing a property of that item. This is essentially a flat-file approach to data management, where everything is contained in one large table.

Access, on the other hand, is a relational database management system. This means that instead of using a single large table, you break your data down into several smaller, interrelated tables. These tables are then linked together using primary keys and foreign keys.

Here's an example to illustrate:

Imagine you have an Excel worksheet that stores information about sales. Each row in the worksheet represents a sale, and you have columns for the Sale ID, Customer Name, Customer Address, Product, and Price.

In Access, you would typically break this down into separate tables:

  • A Customer table, with columns for Customer ID (primary key), Customer Name, and Customer Address.
  • A Product table, with columns for Product ID (primary key) and Price.
  • A Sale table, with columns for Sale ID (primary key), Customer ID (foreign key linking to the Customer table), and Product ID (foreign key linking to the Product table).

This way, you're not repeating the customer or product information for each sale. Instead, you store each piece of information just once, in its own table, and then refer to it using the ID when you make a sale.

This is the key paradigm shift when moving from Excel to Access: moving from a flat data model where everything is in one table, to a relational data model where data is broken down into smaller, related tables.

This approach has several benefits, including:

  • Reduced redundancy: You only need to store each piece of information once, which can save a lot of space if you have a large database.
  • Increased consistency: Because each piece of information is stored only once, you don't have to worry about inconsistencies where the same information is stored in two places and one is updated but the other isn't.
  • More powerful queries: You can create complex queries that pull in information from multiple tables based on the relationships between them.

This does require thinking more carefully about your data and how it's structured, but the benefits in terms of managing and querying your data can be substantial.

Best My Practices

Personally, I think they're the best, though others may disagree.

  • Initial draft generated with the help of ChatGPT
  • Sample data generated with the help of ChatGPT

All original code samples by Mike Wolfe are licensed under CC BY 4.0