SQL Server data types
The definition of a SQL Server data type can be a quick reference for a user of a particular column or variable. The data type of a column or variable is a reference for a user of a column or variable.
WHAT IS THE DATA TYPE?
If you were to create any table or variable, in addition to specifying a name, you also specify the type of data that it will store.
HOW TO USE MS SQL data types
1. Your data will be smoother and cleaner when you define its data type in advance.
The data type also limits the user from entering any unexpected or invalid data.
2. You can put your best foot forward by utilizing system memory effectively. Set the appropriate data type for variables and columns, and your system will only allocate the amount of memory needed.
3. MS SQL offers wide-ranging data types, like dates, binary images, etc
WHY USE DATATYPES?
SQL Server data types offer a wide category of data types according to user needs. You can choose a data type that fits your need. There’s also a variety of dates, binary images, etc:
1) The Name/Surname will always be alphabetical.
2) The “Contact” will always be numeric.
3) From the above figure, you should specify “Name/Surname” as a symbol and “Contact” as an integer.
Obviously, in any application, all fields have one or the other data type. For example, numerical, alphabetic, date, and much more.
Another thing to note is that different SQL Server data types have different memory requirements. So it makes sense to define a column or a variable with the type of data it will store for efficient memory usage.
THE DATA TYPE IS AVAILABLE IN MS SQL
MS SQL Server supports the following categories of data type:
- Exact number
- Approximate numerical
- Date and time
- Character strings
- Unicode Character Strings
- Binary rows
- Other types of data
EXACT NUMBER
These are the data types that will be described below.
Accurate numeric data types
| Data type | Description | Lower limit | Upper limit | Memory |
|---|---|---|---|---|
| BIGINT | It stores integers in the specified range | -2 ^ 63 (-9 223 372, 036 854 775 808) | 2 ^ 63−1 (−9 223 372, 036 854 775 807) | 8 bytes |
| INT | It stores integers in the specified range | −2 ^ 31 (-2,147, 483,648) | 2 ^ 31−1 (-2,147, 483,647) | 4 bytes |
| SMALLINT | It stores integers in the specified range | −2 ^ 15 (−32,767) | 2 ^ 15 (−32 768) | 2 bytes |
| TINYINT | It stores integers in the specified range | 0 | 255 | 1 byte |
| a little | Can take values 0, 1 or NULL | 0 | 1 | 1 byte / 8-bit column |
| decimal | Used for numbers and fixed precision numbers | -10 ^ 38 + 1 | 10 ^ 381-1 | 5 to 17 bytes |
| numeric | Used for numbers and fixed precision numbers | -10 ^ 38 + 1 | 10 ^ 381-1 | 5 to 17 bytes |
| Money | Used monetary data | −922,337, 203, 685,477.5808 | +922,337, 203, 685,477.5807 | 8 bytes |
| small money | Used monetary data | -214,478.3648 | +214,478.3647 | 4 bytes |
EXAMPLES:
DECLARE @Datatype_Int INT = 2
PRINT @Datatype_Int
Exit: 2
Syntax: decimal (P, S)
Here you go,
- Accuracy
- S is the scale
Request:
DECLARE @Datatype_Decimal DECIMAL (3.2) = 2.31
PRINT @Datatype_Decimal
Output: 2.31
APPROXIMATE NUMERICAL
Empirical data is stored as approximate numbers and is mainly used in scientific calculations.
Approximate numeric data type
| Data type | Description | Lower limit | Upper limit | Memory | Accuracy |
|---|---|---|---|---|---|
| Float (n) | Used for a floating precision number | -1.79E + 308 | 1.79E + 308 | Depends on the value of n | 7 digits |
| real | Used for a floating precision number | -3.40E + 38 | 3.40E + 38 | 4 bytes | 15-digit |
Syntax: FLOAT [(n)]
Here n is the number of bits that are used to store a mantissa with a floating-point in a scientific record. By default, n is 53.
When a user defines a data type such as float, n should be between 1 and 53.
SQL Server handles n as one of two possible values. If 1 <= n <= 24, n is treated as 24. If 25 <= n <= 53, n is treated as 53.
Example of a query:
DECLARE @Datatype_Float FLOAT(24) = 22.1234
PRINT @Datatype_Float
Exit: 22.1234
DATE AND TIME
Date and time data is stored here:
Date and Time Data type
| Data type | Description | Storage Size | accuracy | Lower range | Upper Range |
|---|---|---|---|---|---|
| DateTime | Used to specify the date and time from January 1, 1753 to December 31, 9999. Accuracy is 3.33 milliseconds. | 8 bytes | Rounded in .000, .003, .007 steps | 1753-01-01 | 9999-12-31 |
| smalldatetime | Used to specify the date and time from 1 January 0001 to 31 December 9999. It has an accuracy of 100 nanoseconds | 4 bytes, corrected | 1 minute | 1900-01-01 | 2079-06-06 |
| date | Used for storage only dates from 1 January 0001 to 31 December 9999 | 3 bytes, corrected | 1 day | 0001-01-01 | 9999-12-31 |
| time | It is used to store only time values with an accuracy of 100 nanoseconds | 5 bytes | 100 nanoseconds | 00: 00: 00,0000000 | 23: 59: 59,9999999 |
| DateTimeOffset | Similar to the time data, but has a time zone offset | 10 bytes | 100 nanoseconds | 0001-01-01 | 9999-12-31 |
| datetime2 | Used to specify the date and time from 1 January 0001 to 31 December 9999 | 6 bytes | 100 nanoseconds | 0001-01-01 | 9999-12-31 |
Example of request:
DECLARE @Datatype_Date DATE = '2030-01-01'.
PRINT @Datatype_Date
Output: “2030-01-01”
CHARACTER STRINGS
This category refers to the type of characters. It allows the user to define a character SQL Server data types that can be fixed and variable in length. It has four types of data types.
Data type Character strings
| Data type | Description | Lower limit | Upper limit | Memory |
|---|---|---|---|---|
| CHAR | This is a string of characters with a fixed width. It stores no more than 8000 characters. | 0 characters | 8000 characters | n bytes |
| VARCHAR | This is a string of characters with a variable width | 0 characters | 8000 characters | n bytes+ 2 byte |
| VARCHAR (Max) | This is a string of characters with variable width. It stores no more than 1 073 741 824 characters. | 0 characters | 2 ^ 31 characters | n bytes + 2 byte |
| Text | This is a string of characters with variable width. It stores a maximum of 2 GB of text data. | 0 characters | 2 147 483 647 characters | n bytes + 4 byte |
Example of request:
DECLARE @Datatype_Char VARCHAR(30) = 'This is Character Datatype'.
PRINT @Datatype_Char
Conclusion: this is the character’s data type
UNICODE CHARACTER STRINGS
This category stores the entire range of Unicode characters, which uses UTF-16 encoding.
Unicode character string data types
| Data type | Description | Lower limit | Upper limit | Memory |
|---|---|---|---|---|
| NCHAR | This is a fixed-width Unicode string | 0 characters | 4000 characters | 2 times n bytes |
| NVARCHAR | This is a Unicode string of variable width | 0 characters | 4000 characters | 2 times n bytes+ 2 byte |
| NTEXT | This is a Unicode string of variable width | 0 characters | 1 073 741 823 symbol | 2 times the length of the line |
Example of request:
DECLARE @Datatype_nChar VARCHAR(30) = 'This is nCharacter Datype'.
PRINT @Datatype_nChar
Conclusion: this is nCharacter Datatype
BINARY STRING
This category contains a binary string of fixed and variable lengths.
Binary String Data Types
| Data type | Description | Lower limit | Upper limit | Memory |
|---|---|---|---|---|
| Binary | This is a binary string of fixed width. It stores a maximum of 8,000 bytes. | 0 byte | 8000 byte | n bytes |
| VARBINARY | This is a binary string of variable width. It stores a maximum of 8000 bytes. | 0 byte | 8000 byte | The actual length of entered data + 2 bytes |
| Image | This is a binary string of variable width. It stores a maximum of 2 GB. | 0 byte | 2 147 483 647 byte |
Example of request:
DECLARE @Datatype_Binary BINARY(2) = 12;
PRINT @Datatype_BinaryOutput: 0x000C
OTHER TYPES OF DATA
These are the other data types described below:
| Data type | Description |
|---|---|
| Cursor | Its output is the column sp_cursor_list and sp_describe_cursor. Returns the name of the cursor variable |
| String version | This version marks the rows in the table |
| hierarchyid`on | This data type represents the position in the hierarchy |
| Unique identifier | Conversion from a symbolic expression. |
| sQL_VARIANT | It stores the values of data types supported by the SQL server |
| XML | It stores XML data in a column |
| Type of spatial geometry | It represents data in a flat coordinate system |
| Type of spatial geography | It presents data in a circular coordinate system |
| Table | It stores a set of results for further processing |
INTERESTING FACTS!
The amount of data that the CHAR data type can hold is twice as large as the VARCHAR data type, which makes it twice as fast when receiving data.
SUMMARY:
- Each column in a table is defined by its content when creating the table.
- There are six categories, one of which is main. The other categories have subcategories. Nine subcategories are available to help distinguish them.
The views expressed on this blog are those of the author and do not necessarily reflect the opinions of Enteros Inc. This blog may contain links to the content of third-party sites. By providing such links, Enteros Inc. does not adopt, guarantee, approve, or endorse the information, views, or products available on such sites.
Are you interested in writing for Enteros’ Blog? Please send us a pitch!
RELATED POSTS
Maximizing SaaS Database Performance in the Financial Sector with AIOps and Cloud FinOps—Powered by Enteros
- 17 September 2025
- Database Performance Management
Introduction The financial sector is evolving rapidly in the era of digital-first services. Banks, investment firms, insurance providers, and fintech companies are managing unprecedented volumes of transactions, risk models, customer interactions, and compliance data. At the center of this transformation are SaaS databases, which power real-time trading platforms, digital banking, fraud detection systems, regulatory reporting, … Continue reading “Maximizing SaaS Database Performance in the Financial Sector with AIOps and Cloud FinOps—Powered by Enteros”
Optimizing Real Estate IT with AI SQL, Spot Instances, and Cloud Centers of Excellence—Powered by Enteros
Introduction The real estate sector is undergoing a digital revolution. From property search engines and virtual tours to predictive analytics for investment and AI-driven customer engagement, the industry is increasingly reliant on data-driven platforms. At the core of this transformation are databases—the backbone of property listings, mortgage systems, customer relationship management (CRM), IoT-enabled smart buildings, … Continue reading “Optimizing Real Estate IT with AI SQL, Spot Instances, and Cloud Centers of Excellence—Powered by Enteros”
Airline Check-ins Crashing: Passengers Stuck in Digital Queues
Introduction Air travel depends on speed and efficiency—but increasingly, passengers are delayed not at the gate, but in digital check-in queues. Database performance is at the heart of these failures. This article explains why airline IT systems struggle under pressure, the business risks involved, and how better database monitoring prevents costly meltdowns. Why Check-ins Depend … Continue reading “Airline Check-ins Crashing: Passengers Stuck in Digital Queues”
Smart Grids Crashing: Blackouts from DB Delays
Introduction Smart grids promise efficiency, sustainability, and resilience. But when databases powering them lag, the result isn’t just inconvenience—it’s regional blackouts and massive financial losses. In this article, we explore why database delays threaten energy infrastructure and how providers can protect against them. Why Databases Matter in Smart Grids Read moreMongoDB profiler and database performance … Continue reading “Smart Grids Crashing: Blackouts from DB Delays”