Alteryx Data Types: A Comprehensive Guide

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When working with data in Alteryx, it is important to understand the different data types and how they are handled within the platform. Alteryx supports a wide range of data types, each serving a specific purpose. In this article, we will provide a comprehensive guide to Alteryx data types, their characteristics, and usage.

Introduction to Alteryx Data Types

Alteryx provides a rich set of data types that represent different kinds of information. These data types define how data is stored, interpreted, and processed within Alteryx workflows. By understanding the characteristics and usage of each data type, users can effectively work with their data and perform various data manipulation and analysis tasks.

Numeric Data Types

Numeric data types in Alteryx represent numerical values, such as integers and decimals. The common numeric data types include:

  • Integer: Represents whole numbers without decimals, both positive and negative.
  • Decimal: Represents numbers with decimals, allowing for more precision.
  • Double: Represents double-precision floating-point numbers with higher precision than decimals.
  • Byte: Represents a small integer value ranging from 0 to 255.

Numeric data types are often used for performing mathematical calculations, aggregations, and statistical analyses.

String Data Types

String data types in Alteryx represent text or character-based data. The common string data types include:

  • V_String: Represents variable-length strings, which can vary in length.
  • WString: Represents wide character strings, supporting a wider range of characters and Unicode encoding.
  • Blob: Represents binary large objects, typically used for storing binary data like images or files.

String data types are used for working with textual data, manipulating strings, and performing text-based operations.

Date and Time Data Types

Alteryx provides specific data types for representing dates, times, and timestamps. The common date and time data types include:

  • Date: Represents a date without a time component.
  • DateTime: Represents a date and time together.
  • Time: Represents a specific time of day.

These data types are useful for handling temporal data, performing date and time calculations, and conducting time-based analyses.

Boolean Data Type

The Boolean data type in Alteryx represents logical values that can be either true or false. It is often used for conditional operations, filtering data, and logical comparisons.

Spatial Data Types

Alteryx supports spatial data types for working with geographic and spatial data. These data types include:

  • Point: Represents a single point in a two-dimensional space.
  • Line: Represents a line segment or a collection of line segments.
  • Polygon: Represents a closed shape with multiple line segments.
  • Spatial Object: Represents complex spatial objects that can contain multiple points, lines, or polygons.

Spatial data types enable spatial analysis, geospatial operations, and mapping functionalities within Alteryx.

Object Data Type

The object data type in Alteryx represents complex objects or structures that can contain multiple values or properties. It allows for storing and manipulating structured data, such as JSON or XML data.

Handling Missing Values

Alteryx provides options for handling missing or null values within data. Users can choose to replace missing values with specific values, remove rows with missing values, or apply specific logic to handle missing data based on their analysis requirements.

Converting Data Types

In Alteryx, users can convert data from one data type to another using various transformation tools. This enables data type compatibility and ensures data consistency throughout the workflow.


Understanding Alteryx data types is essential for effectively working with data within the platform. By leveraging the appropriate data types, users can perform data manipulations, transformations, and analyses accurately.

Whether it’s numeric data, strings, dates, or spatial information, Alteryx provides a versatile set of data types to support a wide range of data-related tasks. Take advantage of these data types in your Alteryx workflows to unlock the full potential of your data analytics capabilities.

Frequently Asked Question

1. Can I change the data type of a field in Alteryx?

Yes, Alteryx provides tools and functions to convert the data type of a field. Users can easily transform data from one type to another based on their requirements.

2. How does Alteryx handle missing values?

Alteryx offers flexibility in handling missing values. Users can choose to replace missing values, remove rows with missing values, or apply specific logic to handle missing data based on their analysis needs.

3. Can I work with spatial data in Alteryx?

Yes, Alteryx supports spatial data types and provides tools for spatial analysis, geospatial operations, and mapping.

4. Is it possible to perform calculations using date and time data in Alteryx?

Yes, Alteryx provides functions and expressions to perform calculations and manipulations on date and time data, allowing users to derive valuable insights from temporal information.

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