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.
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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.