Joined Datasets

Joined Datasets let you link data from different integrations into one view. This is how you do cross-functional reporting—like comparing billing from QuickBooks against service metrics from ConnectWise.

Creating a Joined Dataset

1. Go to Data Settings → Datasets
2. Click + Add Dataset
3. Select Join Datasets

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The Join Editor

The editor lets you chain datasets together. You start with a “Base Dataset” and extend by attaching more.

1. Base Dataset

Every join starts with a foundation.

• Click Add Base Dataset
• Select a Dataset or Modified Dataset
• Select Columns: Choose which fields appear in the final output
• Filter (optional): Click the filter icon to restrict rows before joining. Good for performance (e.g., only loading “2023” tickets).

2. Adding datasets

Once you have a base, add more data using joins or unions.

Join Dataset (horizontal combination)

Adds columns from another dataset based on a shared key.

1. Click Join Dataset at the bottom of the list
2. Select Dataset: The table to link
3. Join Type:
   • LEFT JOIN (most common): Keep all rows from existing data, add matching info from the new dataset
   • INNER JOIN: Only keep rows that exist in both datasets
   • RIGHT JOIN: Keep all rows from the new dataset, match against existing
   • FULL OUTER JOIN: Keep everything from both sides
4. Join Conditions (ON): Define how tables relate
   • Left Side: Column from any previous table
   • Right Side: Matching column from the new table
   • Text Value Mode: Click “C” to switch to “Text” mode for static values (e.g., join where [Type] equals “Service”)
   • Multiple Conditions: Click Add Condition for complex keys (e.g., match on [Date] AND [Customer ID])

Union All (vertical combination)

Stack data from another dataset on top of existing rows. Useful for combining similar data from different sources (e.g., “Tickets A” and “Tickets B”).

1. Click UNION ALL
2. Select the dataset to append
3. Make sure columns match in meaning and type

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Configuration and settings

Managing columns

• Selection: Check boxes for columns you want to keep
• Reorder: Click Reorder Columns to change field sequence
• Rename: Click Edit Column Labels to alias columns (e.g., rename invoice_id to Invoice Number)

Dataset settings (sidebar)

• Label: Friendly name for dashboards
• Description: Documentation for your team
• Row Limit: Defaults to 1,000,000. Adjust for performance or larger needs.
• Lock Template Updates: Prevents overwrites when re-applying system templates

Saving after removing columns

When you edit an existing Joined Dataset and remove columns from the output, Resplendent checks whether those columns are used elsewhere before saving.

If anything depends on the removed columns, the Columns Being Removed dialog opens. It can show affected Modified Datasets, Joined Datasets, Widgets grouped by dashboard, and Filter Variables.

Choose Cancel to keep editing, or Save Anyway to continue with the save. If the check cannot complete, the save is stopped.

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Best practices

1. Filter early

Use the filter icon on each dataset block. Filtering before joining cuts down processing time significantly.

2. Choose the right keys

Join keys must share the same data type.

• Mismatch problem: Joining a String to a Number gives zero matches
• Solution: Create a Modified Dataset first to convert column types, then use that in your join

3. Handle duplicate column names

If both tables have a [Status] column, the system prefixes them (e.g., Tickets_Status and Projects_Status). Use Edit Column Labels to give them clear names like “Ticket Status” and “Project Status.”

4. Use Modified Datasets as sources

If you need complex cleaning (filling nulls, exploding tags) before joining, create a Modified Dataset first. Then select it as your source in the Join Editor.