Data types

A data type defines the possible values for a field or variable, the operations that can be done on that field, and the way the values of that type are stored.

The following table outlines the main Data360 Analyze data types:

Data type	Description
String	Text values
Unicode	Text values
Boolean	True or false values
Int	Integers or whole numbers in the range - 2,147,483,648 to 2,147,483,647
Long	Whole number in the range -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807
Double	Floating point or decimal values
Date	A date value
Time	A time value
Datetime	A date and time combined, also known as a timestamp. Tip: You can use the node to convert datetimes to local times. If the datetime does not contain the timezone offset information, the timezone is assumed to be local. If the datetime does contain timezone offset information, the Data Converter node will take this into account when processing and adjust the value to local time, accounting for daylight savings if required.

If you're working in one of the Python-based nodes and would like to see how the Python data types map to the corresponding Data360 Analyze data types, see the Python scripting section.

Elements of a data table

Data is commonly stored in a tabular structure, consisting of fields and records:

	Field 1	Field 2	Field 3
Record 1	Field 1 Record 1	Field 2 Record 1	Field 3 Record 1
Record 2	Field 1 Record 2	Field 2 Record 2	Field 3 Record 2
Record 3	Field 1 Record 3	Field 2 Record 3	Field 3 Record 3

Fields

The header row contains a number of fields. A field is a category of information that makes up one part of a record and corresponds to one column in the table.

Records

Each row contains a separate record. A record contains the unique information about a given item, divided into the specified fields. The number of records in a table can be large, up to thousands or millions.