Dataclips

The Dataclip Designer is a module within the application used for the creation and management of dataclips. It can be accessed via Data Studio → Dataclips.

A dataclip is a structured dataset that can be reused throughout the software suite. Dataclips are commonly used as:

• Data sources for form components such as lists and trees
• Data sources for dashboard visualisations
• Persisted datasets managed by dataflows

The designer supports multiple dataclip types, each with its own way of defining and managing data.

Supported dataclip types

Push Dataclip

The Push dataclip allows manual definition of a table-like data structure.

Structure: defined using headers, where each header has a unique name and a value type. Supported value types: Integer, Fraction (floating point), Boolean, Date and Time, Date, Textual, Color, Image, Location.

Properties

Setting	Description
Old data check	Enables a notification for the end user if the data has not been updated recently.
Max count	Limits the maximum number of rows stored in the dataclip.
Unique header	Defines a column that must contain unique values across all rows.
Datetime header	Defines the column used as a timestamp for each row.
Structure version	Determines how header values are stored in the database: see Structure versions below.

Structure versions

• Version 1: Legacy structure: stores all columns as nvarchar(max), regardless of the configured header value type.
• Version 2: Typed structure: uses database column types corresponding to the configured header value types. This significantly improves performance when working with large datasets and filtered data.

Use Structure Version 2 for new dataclips. Version 1 remains available for compatibility with existing dataclips.

Behavior

• Data is typically pushed into the dataclip via dataflows or external sources such as Excel.
• The structure is fixed unless manually updated in the designer.
• The unique header enforces row-level uniqueness.

SQL Dataclip

The SQL dataclip provides a dynamic dataset based on a database query.

Configuration: when creating an SQL dataclip, define a SQL connection and a SQL query. The query result determines the dataclip structure.

Designer features

• Advised Headers: automatically generated based on the query result.
• Type Adjustment: modify the inferred value types per column.
• Advised Changes: a summary of structural changes applied during design.

Properties

Setting	Description
Unique header	Defines a column with unique values.
Datetime header	Defines the timestamp column.

Connections: connections can be created directly from the Dataclip Designer via the Connections menu tab. Supported connection types:

• SQL Connection: requires a connection string, and includes a timeout setting.
• DataHub Connection: used exclusively for Pull dataclips, no additional configuration required at this stage.

Behavior

• The dataclip acts as a logical layer on top of a database query.
• The structure is derived from the query result.
• Lifecycle depends on the underlying database.

Pull Dataclip

The Pull dataclip retrieves data using DataHub logic. It behaves similarly to an SQL dataclip but relies on DataHub configurations instead of direct database queries.

This dataclip type is expected to be remodeled in the future and may change.

Usage

Dataclips are used throughout the platform as reusable data sources:

• Dashboard visualisations
• Form components (lists, trees, selectors)
• Dataflows (input/output datasets)

They act as a central abstraction layer between raw data sources and application features.

Lifecycle & data flow

• Push Dataclips: data is externally pushed (e.g. via dataflows or Excel imports); it persists until overwritten or limited by max count.
• SQL Dataclips: reflect the state of the underlying database query; there is no internal data storage lifecycle.
• General: dataclips themselves are persistent configurations; the structure remains stable unless modified in the designer.

Live View

The Live View feature lets you inspect the current contents of a dataclip. It’s accessible from the Dataclips overview list: each dataclip provides a context menu with a Live View option, which displays the current data stored or returned by the dataclip. This is especially useful for:

• Verifying incoming data for Push dataclips
• Validating query results for SQL dataclips
• Debugging data issues such as missing or incorrectly formatted values

Data formats & binding

When sending or binding data to a dataclip, the data must match the defined value type formats of the headers.

• Push Dataclips: data is sent into the dataclip; the incoming payload must match the configured header types.
• SQL & Pull Dataclips: data is bound from external sources; the returned dataset must align with the expected formats.

If the data does not conform to the expected format or pattern, the value will not be parsed correctly and the field will appear as empty (blank) in the application.

Supported data formats

Date: accepted formats: yyyy-MM-dd, yyyy-MM-dd HH:mm:ss (time portion will be automatically trimmed).

DateTime: accepted formats: yyyy-MM-dd, yyyy-MM-dd HH:mm:ss, yyyy-MM-dd HH:mm:ss:fff, yyyy-MM-ddTHH:mm:ss:fffZ.

Boolean: accepted formats: true / false, 1 / 0.

Location: expected pattern: latitude;longitude;precision;altitude;altitudePrecision. Latitude and longitude are required; precision, altitude, and altitudePrecision are optional. Example: 51.12345;5.98765;10;45;5.

Formatting is strictly validated: any mismatch results in empty values in the UI. Ensure external systems (SQL, APIs, dataflows) output data in the correct format.

Tips

• Use Push dataclips for controlled, application-managed datasets.
• Use SQL dataclips for live database-driven data.
• Use Pull dataclips when integrating with DataHub logic.
• Always configure a Unique header when row uniqueness is required.
• Use a Datetime header for time-based logic and freshness tracking.
• Ensure all data strictly matches expected formats to prevent blank values.
• Use Live View to quickly validate data and debug issues.
• Be cautious with Max count to avoid unintended data loss.