Tile editor

The tile editor is the interface that lets you retrieve, filter, shape and visualize your data. You use the tile editor whenever you want to add or edit a data tile, or when when using the data explorer to experiment with your data streams.

You can add a title and description for your tile at the top of editor.

Configuration flow

The tile editor has two panels. The left-hand panel steps you through retrieving and shaping your data. The right-hand panel decides what happens to it once you have it.

Left-hand steps

Work down the left-hand steps in order:

Data source: Choose where the data comes from.
Data stream: Choose the specific set of data to retrieve.
Objects: Scope the data stream to a collection of objects.
Parameters: Configure the inputs the data stream accepts.
Timeframe: Choose the period the tile displays data for.
Filter | Group | Sort: Shape the raw data before you visualize it.
Columns: Format the columns of the resulting table.

Not every step applies to every data stream, so the editor only shows the ones your chosen data stream supports. A Multiple datasets toggle in the editor header rearranges these steps so that one tile can combine several data streams. See Multiple datasets.

Right-hand tabs

The right-hand panel becomes available once your data loads:

Visualization: Turn the data into a chart by configuring it yourself.
SmartViz: Describe the chart you want and have it built for you.
Monitoring: Watch the data for a state or threshold change.
KPI: Publish the value as a key metric.

Visualization and SmartViz share the same tab. Only one of them is active at a time, and switching between them replaces the tab rather than adding to it.

Data source

The first step to visualizing your data is to choose the data source you want to use. When you add a new data tile to a dashboard, you are presented with the data stream splash screen.

From here, you can quickly select recent data sources, browse all your data sources, and even add new data sources if needed.

For even more convenience, you're also presented with a list of recent data streams, helping you get to your data even faster.

Data stream

The Data Stream tab displays a list containing all of data streams for your currently selected data source and can be filtered using the search and dropdowns.

For each data stream in the list you will see:

The Name of the data stream. You you can sort data by streams A-Z or Z-A by clicking thiscolumn.
The tagged collections of the data stream, displayed in pills to the right.

Many data streams are related to each other as pre-configurations of an encompassing data stream.

For example, the Workflow Runs / In Progress Workflows data stream is a pre-configuration of the Workflow Runs data stream, where the Status parameter has automatically been pre-selected.

To reflect this, pre-configured data streams follow the naming convention of <Primary data stream> / <pre-configuration>.

For help with configuring the data streams specific to your data source, refer to the corresponding data source article:

List of all data source articles for SquaredUp authored plugins

Objects

Use the Objects tab of the tile editor to specify a collection of objects to display data for. You only need to complete this tab if you have selected a Scoped data stream.

Selecting a collection

Do the following to choose the objects for the collection:

Search the list for the objects for those you want to add. By default, every object you have access to is presented with the total selected / available displaying below.
- Use the Search objects box to directly search for objects by their name.
- Use the Filter pane on the left-hand side to select the Data source(s), Source Type(s) and / or Collection(s) to filter the object list by.
  - Click Add filter
    at the bottom of the Filter pane to add additional properties to the filter list.
    When you add a filter it also adds that property as a column in the object list so you can see the values.
    You can also achieve this by clicking the Add column button
    at the top right.
To select the objects you wish to be in the collection you can either tick the objects, or toggle dynamic selection.
- Fixed selection: Returns the specific objects that you manually pick by ticking them. Objects in a fixed scope never change, except when you edit the collection.
- Dynamic selection: Enabled by selecting the checkbox on the table header. This returns all the objects that match the criteria specified in the Filter panel and search. When objects that match are added to or removed from SquaredUp, the objects in the selection will change. Dynamically scoped tiles check which objects match the filter whenever they refresh.
Click Next.

Saving a collection

The collection of objects you specify on this tab can be saved for future use in your workspace by clicking Add collection

in the Filter panel. This opens the Add collection window, pre-populated with the objects you selected.

Collections can also be created and saved via the Objects page, accessed via the left-hand menu. See Collections.

Parameters

Use the Parameters tab of the tile editor to configure your data streams. Not every data stream has parameters to configure (for example, a data stream might simply retrieve a specific set of data), and those that are can vary widely depending on the data stream itself.

For instructions of configuring the parameters for a data stream, refer to the Data streams section for the corresponding plugin.

Using dynamic values in queries

Some data stream parameters (such as the Azure DevOps WIQL Query parameter) require you to enter a query to return data. These queries can made dynamic by using expressions that reference values such as the tile timeframe or the dashboard variable, so that they update based on your settings instead of being hard-coded.

To create a dynamic query, you can insert placeholders using Mustache syntax that automatically get replaced with the dynamic values, allowing your query to reflect the dashboard or tile’s current configuration.

Supported expressions include:

Expression	Description	Resolved value example
`{{timeframe.start}}`	Start time from the dashboard or tile timeframe.	`2024-08-17T14:30:00`
`{{timeframe.end}}`	End time from the dashboard or tile timeframe.	`2024-08-18T15:00:00`
`{{variable1.map((v) => v.displayName).join(', ')}}`	Comma-separated list of display names for selected variable values. The `join` parameter can be changed depending on how your query expects to separate multiple values.	`value1, value2, value3`
`{{variable1[0].key}}`	Key of the first selected item in a variable. Useful for queries that expect single-value input. You can replace key with any property for the object, for example `sourceId`: `{{variable1[0].sourceId}}` See object properties.	`sourceIdExample`

Timeframe

Use the Timeframe tab to choose the timeframe that a tile displays data for. For example, by default a tile's timeframe is set to the dashboard timeframe but you could instead choose to display data from the last 30 days.

No timeframe option

There are many data streams where specifying a timeframe is optional, such as ones which scope to a specific collection and thus don't necessarily need to return results from a specific period.

In these circumstances, the timeframe picker displays the None option, allowing you to return results without considering the tile / dashboard timeframe.

If None is the selected option for the timeframe:

The Timeframe tab in the tile editor displays None.
No tile timeframe pill displays on the tile.
When enabling monitoring or KPI, no timeframe is enforced on the tile (such as when using dashboard timeframe).

Mandatory timeframes

Many data streams, particularly those that accept a query input such as KQL, require you to specify a timeframe. In such cases, the Timeframe tab is not shown and the timeframe must be declared in on the Parameters tab.

Timeframe not supported

In circumstances where specifying a timeframe for a data stream is not supported, the Timeframe tab is not shown.

Referencing timeframes

When configuring the Timeframe tab for data streams (typically such as when configuring custom queries or requests), you will be required to reference the timeframe on the Parameters tab.

To do this, you can enter the following variables to represent the start and end of the timeframe you want to return data for:

Property	Resolved example
`{{timeframe.start}}`	`"2025-03-01T00:00:00.000Z"`
`{{timeframe.end}}`	`"2025-04-01T00:00:00.000Z"`
`{{timeframe.unixStart}}`	`1709251200`
`{{timeframe.unixEnd}}`	`1711929600`

These Mustache-style placeholders support JavaScript, allowing you to format values dynamically based on your needs.

Use this tab to shape and structure your raw data to improve clarity and usability before visualizing it. This process involves:

Filtering, which extracts relevant subsets by applying conditions.
Grouping, which aggregates data based on common attributes.
Sorting, which arranges data in ascending or descending order.

Filtering

Data can be filtered according to whether data in a column meets or does not meet specified text or numerical value conditions.

Multiple filters

You are able to add multiple filter conditions using the following operators:

AND: All conditions must be satisfied (e.g. Status-Equals-Closed ANDType-Equals-Question).
OR: Any condition can be satisfied (e.g. Status-Equals-Pending ORStatus-Equals-Closed).

Available filters

The following options are available when filtering data, which ones display depends on the column type.

Option	Description
Equals	Checks if the value of a field is the same as the specified value. For example,a Status of Active will return results where the status is `Active`.
Not equals	Checks if the value of a field is not equal to the specified value. It returns true if the values are different. For example, a status of Active would return results where the category is not`Active`.
Contains	Returns data if the specified value exists within the field value. For example, example: `URL Contains projects` will return results where the URL includes the word "projects" anywhere in the string.
Doesn't contain	Returns data if the specified value doesn't exist within the field value. For example, example: `URL Doesn't contain projects` will return results where the URL doesn't include the word "projects" anywhere in the string.
Less than	Checks if the value of a field is below the specified value. It is used for numerical or date values. For example, `IncidentsLess than50` would return results where the number of incidents is below 50.
Greater than	Checks if the value of a field is over a specified value. It is used for numerical or date values. For example, `Incidents Greater than 50` would return results where the number of incidents is over 50.
Is more than	Available when working with a date / time column. Checks if the value of a field is older than a given time period. You must additionally specify a time quantity and period, and whether to measure `ago` or `from now`. For example, `Due Is more than 100 days from now` will return results where the due date is later than the current day + 100 days. Similarly, `Due Is more than 100 days ago` will return results where the submitted date is earlier than the current day.
Within last	Filter records that fall within a specific time range from before the current date and time. You must additionally specify a time quantity and period. For example, `Submitted Within last 100 days` will return results where the submitted date is between the current day and 100 days ago.
Within next	Filter records that are within a specified time range after the current date and time. You must additionally specify a time quantity and period. For example, `Event date Within next 7 days` would return results where the event date is within the next week.
Is empty	Returns all data without a date value. Useful for identifying records where data is missing.
Is not empty	Returns all data with a date value.

Grouping

Use the grouping section to group and aggregate data columns.

For example, for AWS cost data you might configure the following settings to display a table or bar chart of cost per label:

Group by:label
Aggregation type: Total
Aggregation column: Amount

Which columns are available depends on the data stream you chose.

Configuring grouping enables different visualizations to be displayed, such as bar chart and donut. For example, grouping tickets by channel allows you to show a donut of how many tickets were logged by email vs web form.

Bucket by

If you group by a time column, and further grouping is possible, the Bucket by dropdown appears. Use this field to control how the time data is grouped, for example by hour, day, month etc.

Aggregation

To aggregate your data, you must select an Aggregation type and a target Aggregation column. For example, if creating bar chart for an Azure Resource Group cost, you could configure the following settings:

Group by: Timestamp
Bucket by: Day
Aggregation type: Total
Aggregate column: Cost

The following lists the available aggregation types.

Aggregate	Description
Total	Sums up all numerical values in a dataset, providing the overall total
Average	Calculates the mean by dividing the total sum by the number of values
Count	Determines the number of entries in a dataset, including duplicates
Distinct Count	Counts only unique values, ignoring duplicates
Max	Identifies the highest value in a dataset
Median	Finds the middle value when data is sorted in ascending order
Min	Identifies the lowest value in a dataset
Mode	Determines the most frequently occurring value in a dataset

Sorting

The Sort section allows you to select one or more columns to sort your date by, in either ascending or descending order.

While this sets the default sort order of data, but you can always click on a column heading to sort the data table on the fly.

To sort by multiple columns, click Add sort by to add a new row of sort fields to the list. This allows you perform more complex sorts, such as sorting data by the data it was created, then sorting those results alphabetically.

Enabling the Top toggle allows you to specify the top n rows of data to display.

Select SQL to shape the tile with a query instead of the filter, group and sort controls. The query runs against the tile’s current data, which is always referenced as dataset.

The editor supports full SQL and is powered by DuckDB. For the query syntax, the editor buttons and IntelliSense, see SQL Analytics.

Columns

Use the Columns tab of the tile editor to format the columns of the table on the Data tab.

SquaredUp automatically defines the metadata retrieved from data streams so the data is assigned the correct data type, however in some circumstances you may want to override this.

For example, when retrieving data using the Web API plugin, scripting, or custom query data streams (such as Splunk Enterprise plugin), the assigned data type may not be quite correct or as you expect.

Column settings

Use the following options to format your columns.

Option	Description
Name	To rename a column, click the current Name value and enter a new one. Columns that can be renamed display the Rename column icon when hovered over.
Type	Click on the row and then select an option from the Type dropdown to change the data type of the column. If any additional options are available to configure, the dropdown is expanded below.
Value	Displays the original value of the column.
Formatted	Displays the formatted value of the column.
Add a copy of this column	Click to duplicate a column. The copied field displays `Copy of [field name]` above its Name.
Remove this column	Click to delete a cloned column.

Custom formatting

Clicking on a row opens the Configure column window, where you can modify the column name and type. Additionally, you can also supply a format expression to manipulate how the column value is displayed.

For example, you might want to attach a label to a value, combine multiple columns together or display the result of some other calculation.

See Expressions for more.

Custom columns

Custom columns allow you to take any data stream and add completely new columns defined by you. You can provide a value expression when configuring the column to calculate the new column value by leveraging the available data.

Note

It is currently not possible to reference a custom column via an expression from inside another custom column.

For example, you might want to create a new URL column by entering an expression that combines an address with a ticket ID, or to map data values in order to create state columns.

To add a new custom column, click the Add button under the columns table and select the Custom option. A new column is then added to the list and the Configure column window opens.

See Expressions for more.

Comparison columns

Comparison columns are used to compare two values, for example you may want to compare the number of tickets raised this month to the number of tickets raised last month. You can choose to show the value as an absolute change (for example, 12 more tickets) or as a percentage change (for example, a 28% increase).

When a column has a Type of Number, the Add comparison

button displays at the end of the row, which you can click to open the Add comparison window.

From this window, if you have multiple columns with a Type of Number, you can create a comparison column by doing the following:

Column A:
Select the first column to compare against. Automatically populated with the column of which you clicked Add comparison
.
Column B:
Select the second column to compare against.
Output:
Select how to display the comparison value. This value is displayed in the Preview field. Choose from:
- Absolute: Show the numerical value of Column A - Column B.
- Percentage: Show the ratio of Column B to Column A as a percent.
Click Add to create the comparison.

Additional options

Some data types have advanced settings that can be configured in the options section, which is displayed whenever you change the data Type or by clicking expand

next to the column Name.

Option

Description

Output Format

Enter a custom format to display date values as a string. Any specified output format is supported. For example, dd/mm/yy, dd/mm/yyyy or d/M/Y.

Note

By default, dates and times are displayed in your local timezone to ensure the data makes sense to you.

Input Format

Enter the format that corresponds to the inputted date. For example, if your data has values such as 27/05/24 01:44 PM, then the Input Format should be set to dd/MM/yy hh:mm aa.

Any input format is supported, however if you have a custom input format that is missing any time zone information, the input is always assumed to be UTC.

Note

This field is required if the data strings for the column are not ISO-8601 formatted. For example, 2024-09-09T13:52:25.281Z.

Currency

Select the currency to display the value in. This does not convert the currency value.

Decimal Places

Number columns automatically show the right number of decimal places: whole numbers (IDs, counts, durations) show none, values with up to one decimal place show one, and everything else shows two. To override this, enter a specific value between 0 and 20.

Thousands Separator

Controls whether large numbers display a separator (typically a comma or period). This is on by default, so existing tiles are unaffected. Turn it off for columns holding identifiers where a separator would be misleading, for example a ticket ID showing as 123,456 instead of 123456.

Link Text

Specify the text of the URL links in the column.

Format as duration

Toggle between displaying the time value in minutes and seconds or seconds.

Map Values to States

You can map the states you're getting back from your data to the states SquaredUp expects.

SquaredUp expects the following values for states to be able to show Status Blocks in the correct matching color:

`success`	green
`warning`	yellow
`error`	red
`unknown`	gray

If your data uses different values for states, you can map them to the values SquaredUp expects.

Tip: Any state value that SquaredUp doesn't recognize gets automatically set to unknown. You can usually just leave out the unknown state from your mapping and just specify the other three states.

Multiple datasets

A single tile normally draws on one data stream. Enable the Multiple datasets toggle at the top of the tile editor to build a tile from several, then combine them with a SQL query before you visualize the result.

Turning the toggle on rearranges the editor:

The left-hand panel becomes a list of datasets. Each one expands to show its own complete set of steps, from Data source through to Columns.
Click Add dataset below the list to add another.
A SQL entry sits at the bottom of the list. Expanding it adds a Query step, where you write the query that combines your datasets, and a second Columns step that formats the query’s output rather than any one dataset.
The data panel gains a tab per dataset, plus a SQL Output tab showing the combined result.

Note

Two steps are labelled Columns once the SQL entry is expanded. The one inside a dataset formats that dataset. The one under SQL formats the combined output.

If you had already selected a data stream and objects before enabling the toggle, that configuration becomes dataset1 and the SQL > Query tab opens immediately.

For writing the query itself, including how to reference each dataset and combine them with a JOIN, see SQL Analytics.

Rename datasets

Click a dataset’s tab to make its name editable. Press Enter to save the change or Escape to cancel. If you are looking at Zendesk tickets, you might rename dataset1 to tickets.

Note

The new name must be alphanumeric, with no spaces or punctuation. At the point of renaming, if the SQL query is unmodified, the name of the dataset is automatically updated in the SQL query.

Dataset buttons

Use the following buttons to create new, copy existing and delete datasets.

Button	Description
Add dataset	Click to create an additional dataset. The Data Stream tab displays and you can progress through data stream configuration flow as you would in the default tile editor view. For each new dataset you add, a new tab displays with a sequential name (dataset2, dataset3 etc.) and can be configured independently as required.
Hover over a dataset > select more > Clone	Click to create a new dataset from the properties of the one you selected. The new dataset takes the name of the original dataset, suffixed with "Copy" (for example, ticketsCopy).
Hover over a dataset > select more > Delete	Click to permanently remove a dataset.

Turn the toggle off

Disabling Multiple datasets keeps only the first dataset and discards the rest, so the editor asks you to confirm. The tile configuration may need adjusting afterwards.

Visualization

Visualization settings are configured on the Visualization tab of the right-hand panel. Which visualizations are offered depends on the data available. For example, Line Graph is only offered if there is time series data in your dataset. See Visualizations for each type.

Where SmartViz is available, this tab also offers a Use SmartViz button at the top, once your data has loaded.

SmartViz

Note

AI features must be enabled for your tenant. An admin can turn these on in Settings > AI.

SmartViz replaces the Visualization tab rather than sitting alongside it. Click Use SmartViz and the tab becomes the SmartViz composer, where you describe the chart you want instead of configuring it by hand.

If the tile already has a chart, SmartViz converts it into the equivalent spec, so you start from what you had. If it does not, the engine builds one from scratch out of whatever data the tile is pulling.

Everything outside the tab behaves the same either way. The Monitoring and KPI tabs are unaffected, and so is every step in the left-hand panel.

Switching back to the classic Visualization tab discards the tile's SmartViz spec and its request history, and you can't undo it. SquaredUp asks you to confirm first, unless the tile has no spec and no requests yet.

For chart types, prompting, and the settings SmartViz exposes, see SmartViz visualizations.

Monitoring

You can enable and create monitors for your data tiles on the Monitoring tab of the tile editor.

These monitors let you watch for changes to your configured data and trigger notifications and status rollups whenever a state, threshold or scripted condition is met.

For detailed information on configuring monitoring, see Monitors.

KPI

KPIs can publish key metrics within a workspace and also aggregate at a higher level for complete oversight.

Defining, monitoring and evaluating KPIs is a practice followed by virtually every organization. In your organization there may be KPIs such as DORA metrics, Service Level Objectives or Cloud costs. SquaredUp allows you to visualize, monitor, and roll up these values - giving you complete oversight of your key metrics.

KPIs are enabled and configured on the KPI tab of the tile editor.

For detailed information on configuring KPIs, see KPIs.

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