Import and update data

Overview

Administrators can easily import data into Skedulo from anywhere that supports CSV files.

Import data

Create an import task

To import your data, you must first create an import task.

To create an import task:

  1. On the Data import/export page, click Create Task.
  2. From the task menu, select Import. The Import wizard opens on the Task details page.

Task details

Fill in the task details to specify whether you want to insert new or update existing records, and to specify into which object you want to import the records.

  1. Select an Operation Type. You can choose to create new records or update existing records.
    • Insert — Select this option to create new records.
    • Update — Select this option to update existing records.
  2. From the Object dropdown list, select the object into which you wish to import the data.
  3. Click Browse and locate the CSV file you want to import.
  4. Click Continue.

The Import data page with numbered callouts to illustrate the steps described.

Field mappings

Map your CSV columns to your Skedulo fields. Fields are ordered based on the columns in the CSV file, and any fields that match existing Skedulo fields will map automatically.

The field mapping stage of the Import data page with a selection of fields that need mapping.

Use the quick find at the top of the selection dropdown to search through your Skedulo fields.

The Map to Skedulo field dropdown menu showing some of the available fields.

Review

The final step is the Review page, which provides a brief summary of the task details.

If required, you can specify the date and time formats and time zone that is used in the data you are importing. This will ensure that the data is correctly converted when it is imported. See the timezone field section below for more information about the impact these fields can have on your data.

The Review stage of the Import data page showing the task details and the advanced date and time options.

Click Create task to save your task and run it immediately.

Understand the timezone field

The Advanced fields in the Review page are important for two parts of the importing process:

  1. For the data loader to understand the date and time fields in your file: If the Date-time format field is not set to the default (UTC) and a timezone is selected, then the data loader will use the information in these fields to understand the date and time data in your file, which it needs for the next step.

  2. Converting the date and time data: When the data is imported into Skedulo, it is converted from the timezone specified to match the timezone of the jobs and resources in the system.

The following examples demonstrate how the Date-time format and Timezone fields impact the data being imported:

Scenario 1
Date-time format field value YYYY-MM-DDTHH:mm:ss.SSSZ

This is UTC, which is the default value. The Z indicates that UTC is used in the CSV file being imported.
Timezone field value N/A

Any Timezone field selection will not be taken into account because the Date-time format field already indicates UTC is in use.
Time in CSV file
  • Start: 2023-07-31T10:00:00.000Z (July 31, 2023 at 10:00)
  • End: 2023-07-31T11:00:00.000Z (July 31, 2023 at 11:00)
Time in Skedulo after import
  • If the work’s region is Berlin (UTC+2), then the start date-time would be 2023-07-31T12:00:00.000 (July 31, 2023 at 12:00), and the end would be 2023-07-31T13:00:00.000 (July 31, 2023 at 13:00)
  • If the work’s region is Ho Chi Minh (UTC+7), then the start date-time would be 2023-07-31T17:00:00.000 (July 31, 2023 at 12:00), and the end would be 2023-07-31T18:00:00.000 (July 31, 2023 at 13:00)
Explanation The value imported will depend on the work’s region.

Because we are using UTC in the CSV file, the time after converting is unchanged.

When the data is imported, the data loader takes the work’s region into account. Therefore, in this example, the system is converting UTC’s 10AM to Berlin or Ho Chi Minh’s equivalent local time.

Scenario 2
Date-time format field vale MM-DD-YYYY HH:mm:ss (Or any format other than YYYY-MM-DDTHH:mm:ss.SSSZ)
Timezone field value Europe/London (UTC+1)
Time in CSV file
  • Start: 07-31-2023 10:00:00 (July 31, 2023 at 10:00)
  • End: 07-31-2023 11:00:00 (July 31, 2023 at 11:00)
Time in Skedulo after import
  • If the work’s region is Berlin (UTC+2), then the start date-time would be 2023-07-31T11:00:00.000 (July 31, 2023 at 11:00), and the end would be 2023-07-31T12:00:00.000 (July 31, 2023 at 12:00)
  • If the work’s region is Ho Chi Minh (UTC+7), then the start date-time would be 2023-07-31T16:00:00.000 (July 31, 2023 at 16:00), and the end would be 2023-07-31T17:00:00.000 (July 31, 2023 at 17:00)
Explanation Because Europe/London was selected as the timezone in the CSV file, the data loader will convert London’s 10AM to the corresponding time in Berlin or Ho Chi Minh.

Update data

When updating data, you must provide the Skedulo record UID(s) you wish to update.

There are two ways to locate the ID of a Skedulo record:

  • Run an export operation to retrieve object data (records). Each row in the export will contain a “UID” column which is the record ID.
  • Use GraphQL to query the object and return the UID in the response. To learn more about GraphQL queries, please see GraphQL queries.

Task statuses

Status Description
Processing The task is running (importing or exporting data).
Completed The task has finished running and all the records are processed (with or without errors).
Canceled The task has stopped for one of the following reasons: an administrator cancelled the process; there has been a connectivity failure; or an unexpected issue has caused the task to stop.