# Dataclips
**Category:** Data studio
**Version:** 1.0
**Last updated:** April 10, 2026
**Author:** Any2Info
***
### Description
The Dataclip Designer is a module within the application used for the creation and management of dataclips. It can be accessed via **Datastudio > 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**
The structure is defined using **Headers**, where each header contains:
* A **unique name**
* A **ValueType**
Supported ValueTypes:
* 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 |
**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, the user must define:
* A **SQL connection**
* A **SQL query**
The query result determines the dataclip structure.
**Designer Features**
* **Advised Headers**\
Automatically generated based on the query result
* **Type Adjustment**\
Users can modify the inferred ValueTypes per column
* **Advised Changes**\
Displays 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**
* 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)
* Data persists until overwritten or limited by max count
* **SQL Dataclips**
* Reflect the state of the underlying database query
* No internal data storage lifecycle
* **General**
* Dataclips themselves are persistent configurations
* Structure remains stable unless modified in the designer
***
### Live View
The **Live View** feature allows users to inspect the current contents of a dataclip.
* Accessible from the **Dataclips overview list**
* Each dataclip provides a **context menu** with a *Live View* option
* 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 **ValueType 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
* 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
Requirements:
* **latitude** → required
* **longitude** → required
Optional:
* precision
* altitude
* altitudePrecision
Example: 51.12345;5.98765;10;45;5
***
#### Notes
* 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 & Best Practices
* 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 **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
***
### Changelog
| Version | Date | Change |
| ------- | -------------- | ----------------------------------- |
| 1.0 | April 10, 2026 | Initial documentation version added |
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