---
title: "SQL Data Types: Reference and Best Practices"
description: "SQL data types define what values columns accept, how those values are stored, and how databases process them, making type selection central to schema design. The reference covers numeric, character, date/time, and specialized types, noting that MySQL, PostgreSQL, SQL Server, and Oracle differ in naming, precision, limits, and storage requirements. It recommends choosing the smallest type that safely accommodates expected values, checking range, precision, NULL requirements, string length, and time-zone needs before deployment. Examples contrast CHAR with VARCHAR, DECIMAL with FLOAT, DATE with TIMESTAMP, and integer sizes such as TINYINT, INT, and BIGINT; indexes and distributed engines also benefit from right-sized data. Sample data, execution-plan analysis, storage measurements, schema audits, and documentation help validate choices, identify waste or overflow risks, and preserve reasoning behind edge cases."
---

# SQL Data Types: Reference and Best Practices

[Databricks](https://yomu.fyi/company/databricks) · Databricks Staff · Sep 8, 2026

**Type:** Explainer

## Summary

SQL data types define what values columns accept, how those values are stored, and how databases process them, making type selection central to schema design. The reference covers numeric, character, date/time, and specialized types, noting that MySQL, PostgreSQL, SQL Server, and Oracle differ in naming, precision, limits, and storage requirements. It recommends choosing the smallest type that safely accommodates expected values, checking range, precision, NULL requirements, string length, and time-zone needs before deployment. Examples contrast CHAR with VARCHAR, DECIMAL with FLOAT, DATE with TIMESTAMP, and integer sizes such as TINYINT, INT, and BIGINT; indexes and distributed engines also benefit from right-sized data. Sample data, execution-plan analysis, storage measurements, schema audits, and documentation help validate choices, identify waste or overflow risks, and preserve reasoning behind edge cases.

## Context

Choosing SQL data types affects data integrity, storage consumption, query performance, scalability, and the maintainability of database schemas. The appropriate choice varies with value ranges, precision, string length, temporal requirements, NULL handling, and differences between database systems.

## Approach / What changed

The reference organizes types into numeric, character and string, date and time, and specialized categories. It advises selecting the smallest safe type, comparing alternatives such as CHAR and VARCHAR or DECIMAL and FLOAT, testing proposed schemas with sample data, measuring storage and query performance, analyzing execution plans, auditing existing schemas, and documenting range-related decisions.

## Takeaways

- CHAR reserves its declared length and pads shorter values, while VARCHAR uses space based on the stored value; fixed-length data favors CHAR, whereas variable-length data favors VARCHAR.
- DECIMAL stores exact values and is recommended for monetary or auditable calculations, while FLOAT stores approximate values and is suited to cases where small rounding errors are acceptable.
- Using a type that is too small can cause overflow, while using an unnecessarily large type wastes storage; schema testing and execution-plan analysis can expose these problems before or after deployment.

**Tags:** [Databases](https://yomu.fyi/topic/databases), [Performance](https://yomu.fyi/topic/performance), [SQL](https://yomu.fyi/topic/sql)

- Source: [Databricks](https://www.databricks.com/blog/sql-data-types)
- Source URL: https://www.databricks.com/blog/sql-data-types
- Ingested by Yomu: 2026-09-09T00:00:55.587Z

[Read original post](https://www.databricks.com/blog/sql-data-types)