Data Types Overview

Explore core BigQuery data types and how they are used when working with large and diverse datasets. Choosing the correct data type is critical for accurate analysis, function compatibility, and correct interpretation of values, especially in global and multi-source data environments.

Core Scalar Types

String, Int, Float, and Boolean are the most commonly used data types:

• String stores text values such as names, labels, or categories;
• Int represents whole numbers;
• Float stores decimal values and is commonly used for prices, metrics, or measurements;
• Boolean represents true or false conditions.

Using the correct type is essential. For example, numeric operations cannot be performed on strings, and date functions cannot be applied to boolean values.

Date and Time Types

BigQuery provides several date-related types, each serving a specific purpose:

• Date stores calendar dates without time;
• DateTime includes both date and time, but no time zone;
• Timestamp represents an exact moment in time and includes time zone awareness.

Timestamps are especially important when working with global datasets. Comparing events from different regions, such as the UK and New York, requires converting data to a shared time zone to ensure consistency.

Nested and Repeated Types

Struct and Array enable working with complex data structures:

• Struct groups multiple named fields into a single logical object;
• Array stores an ordered list of values.

Structs are useful for nested attributes, while arrays are ideal when order or count matters, such as calculating the number of elements using ARRAY_LENGTH.

Function Compatibility

Functions in BigQuery are designed to work with specific data types:

• Arithmetic operations apply only to numeric types;
• EXTRACT works with date and time types;
• ARRAY_LENGTH applies exclusively to arrays.

Understanding these relationships helps prevent errors and leads to more efficient query design.

Selecting the appropriate data type — and knowing which functions work with it — has a direct impact on query correctness, performance, and reliability. This becomes especially important when analyzing global datasets or working with nested and mixed data structures.