Datasets

Datasets are the raw tables synced from your integrations. They’re the foundation for everything else—widgets, transformations, joins.

Understanding datasets

When you connect a source, Resplendent Data creates a dataset for each table or API endpoint. A ConnectWise integration might give you separate datasets for “Tickets,” “Time Entries,” and “Companies.”

Dataset properties

Property	What it shows
Sync Status	Whether the last sync succeeded, is running, or failed
Row Count	Total records currently stored
Last Sync	Timestamp of the last successful refresh
Columns	Available fields (Ticket ID, Summary, Status, etc.)

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Viewing and managing datasets

Accessing the dataset list

1. Click Data Settings in the sidebar
2. Select the Datasets tab

This gives you an overview of all synced data.

Dataset details and preview

Click any dataset name to open its detail view:

• Preview: See sample data to verify it looks right
• Columns: View data types (String, Number, Date, etc.) for each field
• Sync History: Review recent sync performance and any errors

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Creating a dataset

When you add a new dataset from a connection, the editor opens to a full-screen dataset picker.

Picking a dataset

The picker has two panes:

• Left — A filterable list of all tables, views, and datasets available on the connection. Click a row to select it.
• Right — A live data preview of the selected dataset (up to 100 rows). The column count and row count appear above the grid.

Once you’ve confirmed the right dataset, click Use this dataset in the top-right corner to move to the column configuration step.

Configuring columns

After selecting a dataset, the editor shows each column as a row with three controls:

Control	What it does
Checkbox	Include or exclude the column from the synced dataset
Type selector	Override the detected data type (String, Number, Date, etc.)
Settings icon (gear)	Open per-column settings

Use the filter input at the top to search by column name or custom label. The Select All checkbox toggles all non-excluded columns at once.

Columns that are required (primary key, order column, virtual primary key columns) have their checkboxes disabled and show a tooltip explaining why.

Per-column settings

Click the gear icon on any included column to open its settings dialog. Available options:

• Custom name — A display label shown in widgets instead of the raw column name
• Column link — A URL pattern that makes cell values clickable links
• Timezone — For datetime columns, override the assumed timezone
• ID column — Mark the column as an identifier to suppress number formatting in previews
• Exclude column — See below

Excluding a column

The Exclude Column toggle in per-column settings locks a column out of the dataset permanently. An excluded column:

• Does not sync, even if a dataset template tries to add it
• Appears dimmed in the column list with its controls disabled
• Can be un-excluded at any time by opening column settings and toggling it off

This is different from unchecking the column’s checkbox. Unchecking just removes the column from the current sync selection; a template can still add it back later. Excluding prevents templates from adding it.

Note

Primary key and order columns cannot be excluded — the toggle is disabled for those columns.

Saving

When you’re done configuring columns, click save & close to sync and save the dataset. If the primary key or order column is not selected, the options dialog opens automatically so you can set them before saving.

If you remove columns from an existing dataset, Resplendent checks for downstream dependencies before saving. When affected items are found, a Columns Being Removed dialog lists the removed columns and any impacted modified datasets, joined datasets, widgets, dashboards, or filter variables.

From that dialog:

1. Click Cancel to go back and review the column change
2. Click Save Anyway to save the dataset with the column removal

If the dependency check cannot run, the save is stopped and you will see an error message.

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Syncing data

Initial sync

The first sync after connecting an integration pulls your full history. This can take a while for large accounts.

Incremental sync

After the initial sync, most integrations switch to incremental mode—only pulling records created or modified since the last sync. Non-incremental datasets always perform full syncs because they lack a reliable primary key, last-updated column, or change-filter mechanism.

Manual refresh

Need the latest data now? Click Sync Now on any dataset. You don’t have to wait for the scheduled refresh.

Tip

Manual syncs work on all tiers, including Free Forever (which doesn’t have automated scheduling).

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Dataset access control (Professional+)

On Professional, Business, and Starship tiers, admins can control which users see which datasets. This is useful for:

• Limiting access to sensitive financial data
• Showing only relevant datasets to specific teams
• Preventing accidental changes to critical data sources

How it works

Admins configure access per user in User Management:

1. Go to Company → Users
2. Edit a user
3. Click Configure Permissions
4. Set dataset access mode:
   • Full access - All datasets visible (default)
   • Blacklist - Hide specific datasets
   • Whitelist - Show only specific datasets

For blacklist or whitelist modes, set each dataset to Read or Read & Write:

• Read - User can view and use the dataset to create widgets, modified datasets, and joined datasets, but cannot edit the dataset
• Read & Write - User can view and use the dataset to create widgets, modified datasets, and joined datasets, and can also edit the dataset

Users only see datasets they have access to in the dataset list, widget selectors, and data lineage. Admins always see all datasets.

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Dataset limits

Performance limits vary by tier:

Tier	Dataset limit	Recommended row max
Free Forever	5	100,000
Starter	60	500,000
Professional	500	1,000,000
Business / Starship	Unlimited	Scalable

Note

For datasets with millions of rows, use Modified Datasets to aggregate or filter the data before building widgets.

Next steps

• Transforming data with Modified Datasets
• Combining data with Joined Datasets
• Using Filter Variables