# Mapping input fields

> :::note
For the selected table, the required input fields for the table are displayed in the Required fields section on the **Fields** page.
:::

:::note
For the selected table, the required input fields for the table are displayed in the Required fields section on the **Fields** page.
:::

The source fields detected in the input table are automatically mapped to the corresponding fields in the target table.

1. Make sure each field is mapped to the correct target field. If required, select a different field from the **Target fields** list to correct the mapping.
2. Select **Next** to continue.

## Configuring input fields

The settings for the target input fields are automatically detected and you just need to check them.

Follow these steps to edit the settings for an input field.

1. Locate the field you want to configure and select the Edit field icon to open the **Edit field** panel for the selected field.
2. Edit the settings as desired and select **Save**.

The following table describes the table settings.

| Setting | Description |
| --- | --- |
| Name | The name of the field. Name is a mandatory field. |
| Autodetect | Allows you to identify the field type in the input file and automatically apply the detected field type for the field in the target table. |
| Type | The data type of the field: Text, Integer, Decimal, Boolean, Date, or Datetime. Depending on field type, you must specify parse settings to configure the field. |
| Mandatory | Option to define the field as mandatory. If selected, the field is required when publishing or importing the process app — an error is thrown if the field is missing. If not selected, the field is optional and added with NULL values when missing, so that subsequent SQL queries do not fail. |
| Unique | Option to define the field value to have a distinct or unique value for each record. |
| Not NULL | Option to define that the field must have a value for each record. The field cannot be left empty or filled with a NULL value. |

## Parse settings for field types

The following table describes the available parse settings for the different field types.

| Field type | Parse settings |
| --- | --- |
| Integer | Thousand separator: None, Dot (.), or Comma (,). |
| Decimal | Decimal separator: Dot (.) or Comma (,). Thousand separator: None, Dot (.), or Comma (,). |
| Boolean | True value: `TRUE` or `1`. False value: `FALSE` or `0`. **True value** and **False value** are mandatory settings and must be different. |
| Date | Date format. Refer to **Example parse settings for Date formats**. |
| Datetime | Date time format. Refer to **Example parse settings for Datetime formats**. |

### Example parse settings for Date formats

| **Format** | **Example** |
| --- | --- |
| `yyyy-mm-dd` | `2025-04-05`  `2025-4-5` |
| `mm/dd/yy` | `04/05/25`  `4/5/25` |
| `mm/dd/yyyy` | `04/05/2025`  `4/5/2025` |
| `mm-dd-yyyy` | `04-05-2025`  `4-5-2025` |
| `dd-mm-yyyy` | `05-04-2025`  `5-4-2025` |
| `yyyy/mm/dd` | `2025/04/05` |

### Example parse settings for Datetime formats

| **Format** | **Example** |
| --- | --- |
| `yyyy-mm-dd hh:mm:ss[.nnn]` | `2025-04-05 14:30:45.123`  `2025-4-5 14:30:45.123` |
| `yyyy/mm/dd hh:mm:ss[.nnn]` | `2025/04/05 14:30:45.123`  `2025/4/5 14:30:45.123` |
| `mm/dd/yyyy hh:mm:ss[.nnn]` | `04/05/2025 14:30:45.123`  `4/5/2025 14:30:45.123`  `04/05/2025 14:30:45`  `4/5/2025 14:30:45` |
| `yyyy-mm-ddThh:mm:ss[.nnn]` | `2025-04-05T14:30:45.123`  `2025-4-5T14:30:45.123` |
| `mm-dd-yyyy hh:mm:ss[.nnn]` | `04-05-2025 14:30:45.123`  `4-5-2025 14:30:45.123`  `04-05-2025 14:30:45`  `4-5-2025 14:30:45` |
| `dd-mm-yyyy hh:mm:ss[.nnn]` | `05-04-2025 14:30:45.123`  `5-4-2025 14:30:45.123`  `05-04-2025 14:30:45`  `5-4-2025 14:30:45` |
| `yyyy-mm-ddThh:mm:ss[.nnn]+00:00`<sup>*</sup> | `2025-04-05T14:30:45.123+02:00`  `2025-04-05T14:30:45-03:00`  `2025-04-05T14:30:45`  `2025-4-5T14:30:45.123+02:00`  `2025-4-5T14:30:45-03:00`  `2025-4-5T14:30:45Z` |
| `yyyy-mm-ddThh:mm:ss[.nnn]+0000`<sup>*</sup> | `2025-04-05T09:30:00+0000`  `2025-04-05T09:30:00.123+0000` |
| `yyyy-mm-dd hh:mm:ss[.nnn]+00:00`<sup>*</sup> | `2025-04-05 09:30:00+00:00`  `2025-04-05 09:30:00.123+00:00` |
| `yyyy-mm-dd hh:mm:ss[.nnn]+0000`<sup>*</sup> | `2025-04-05 09:30:00+0000`  `2025-04-05 09:30:00.123+0000` |
| `dd/mm/yyyy hh:mm:ss[.nnn]` | `05/04/2025 14:30:45.123`  `5/4/2025 14:30:45.123` |
| `mm/dd/yy hh:mm:ss[.nnn] AM/PM` | `04/05/25 02:30:45 PM`  `4/5/25 02:30:45 PM` |

*) Timestamps that include time zone information are automatically converted to UTC during data ingestion.