data types

Complete Guide to SQL Server Data Types

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SQL Server provides a solid set of system data types that handle everything from storing tiny integers to massive text blobs. Understanding these types is an important part of designing efficient databases, mainly because picking the right data type can save storage space and improve query performance.

This article breaks down all the data types available in SQL Server (as of SQL Server 2025), organized by category. Each type includes its max length, precision, scale, and whether it can be nullable.

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What is a CRDT?

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A CRDT (Conflict-Free Replicated Data Type) is a special type of data structure designed for distributed systems that guarantees multiple replicas of data will eventually converge to the same state without requiring coordination between nodes. Even when different users simultaneously modify the same data in different locations, CRDTs automatically resolve conflicts in a mathematically consistent way that ensures all replicas eventually agree.

The main insight behind CRDTs is that certain operations can be designed to be commutative, meaning the order in which you apply them doesn’t matter. If operation A followed by operation B produces the same result as operation B followed by operation A, you can apply updates in any order and still reach the same final state. This property eliminates the need for complex conflict resolution logic.

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How to Choose Appropriate NUMERIC Precision to Avoid Overflow in SQL Server

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When working with SQL Server, numeric precision can become a silent troublemaker if you’re not careful. Overflow errors happen when a number exceeds the storage capacity of the column data type. Unlike other errors that are easy to catch in testing, numeric overflow often shows up unexpectedly in production, which can be costly. Understanding how to pick the right precision and scale for your NUMERIC or DECIMAL columns can save you headaches down the road.

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Why You Should Consider More than Expected Maximum Values When Choosing SQL Server Column Datatypes

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When designing a SQL Server database, one of the first tasks is deciding what datatype to use for each column. A common rule of thumb is to choose a datatype that fits the largest value you expect to store. For example, if you know an INT will comfortably hold all expe1cted order quantities, then it seems like a safe choice.

But that logic can fall short in some cases. It completely ignores an important consideration – aggregate queries.

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How to Validate Column Data Types Before Inserting Data in SQL Server

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One of the most common causes of errors in SQL Server is trying to insert the wrong type of data into a column. If a column expects an integer but you push in a string, SQL Server will either throw an error or attempt an implicit conversion that may not work the way you expect. This is more common when data comes from external sources like APIs, flat files, or user inputs, where you don’t always control the formatting. Validating data types before inserting not only prevents runtime issues but also keeps your tables clean and predictable.

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5 Ways to Get the Data Type of Columns Output by a Query in DuckDB

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Usually when we run a SQL query it’s important to know the data type of the columns returned by that query. If we’re familiar with the data, then we might instinctively know the data type of each column. But even then, we might only have a general idea, such as “it’s a date” or “it’s a number” without knowing the exact details.

Fortunately, DuckDB provides us with several ways to find out the data type of the columns returned by a query.

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Understanding Type Affinity in SQLite

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SQLite is a lightweight, self-contained database engine renowned for its simplicity and flexibility. One of its unique features is the way it handles data types through type affinity, which determines how SQLite associates values with storage classes.

Unlike many database systems that enforce strict type constraints, SQLite’s type affinity system is more flexible, accommodating a broader range of data.

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Data Types that SQLite Allows for Strict Tables

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SQLite is a lightweight, self-contained SQL database engine known for its simplicity and versatility. In version 3.37.0, SQLite introduced strict tables, offering stricter type enforcement compared to its regular tables.

This feature allows developers to define tables with precise data types, ensuring better data consistency.

In this article, we’ll look at the six supported data types for strict tables—INT, INTEGER, REAL, TEXT, BLOB, and ANY—and provide simple examples to illustrate their usage.

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An Introduction to Strict Tables in SQLite

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SQLite is widely known for its simplicity, flexibility, and lightweight architecture. One feature that sets it apart from most other SQL databases is its dynamic typing system, which allows columns in a table to store data of any type, regardless of their declared type.

While some developers welcome this departure from the traditional SQL approach, others find it extremely problematic, due to its non-enforcement of data types, which could potentially lead to data integrity issues.

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