Getting Started with DFS

This guide is for a new DFS user who needs to connect one source system and verify that data can flow into FactVerse.

The goal is a small working loop:

Create connector -> test connection -> browse source -> preview values
  -> map one field -> sync -> check sync history and data quality

Prerequisites

Before starting, prepare the following:

Requirement	Notes
Tenant access	You need access to the customer or project tenant.
Permissions	You need dfs:read and dfs:write for the first connector.
Source details	Prepare the source endpoint, file, topic, table, credentials, or network information.
Target identity	Prepare the asset ID, point ID, equipment ID, or target object that the source data should map to.
Owner	Know who can confirm source meaning, units, and expected value ranges.

Use DFS permissions when access is blocked.

Step 1: Open Data Integration

Open the FactVerse application and go to:

Data Integration > Connectors

If no connectors exist, the page shows an empty state and an action to add the first connector.

Step 2: Add a connector

Select Add Connector.

Choose a source type. Common starting choices are:

Source type	Typical use
CSV	First import, offline test data, exported historian data.
REST	Enterprise APIs, cloud services, custom integrations.
MQTT	IoT topics and streaming payloads.
OPC UA	Industrial equipment and automation systems.
BACnet	Building automation and facility systems.
Modbus	Industrial devices with register maps.
JDBC	Databases, historians, reporting tables.

Use a template if one matches the source. Otherwise configure the connector manually.

Step 3: Configure connection settings

Enter a clear connector name. The name should identify the site, source system, and purpose.

Examples:

• Plant A BMS chilled water points
• Line 3 PLC OPC UA
• Compressor historian export
• Work order REST feed

Choose a sync strategy:

Strategy	Use when
Realtime	The source can stream or subscribe to changes.
Interval	DFS should poll the source on a schedule.
On demand	The source should be read only when a user or job triggers sync.

Step 4: Test before saving

Use Test Connection before saving the connector.

A successful test means DFS can reach the source with the provided configuration. Mapping review, source preview, sync history, and data quality checks are still required before production use.

If the test fails, check:

• credentials;
• network allowlists;
• endpoint URL or host;
• protocol security settings;
• database or topic names;
• source-side permissions.

Step 5: Save and start

Save the connector.

If the connector should begin collecting data immediately, start it from Connector Detail or choose the option to enable it after creation. For cautious rollout, save it disabled, review mappings, then start it later.

Step 6: Browse and preview source data

Open the connector detail page.

Use source browse and preview to confirm:

• the expected equipment, tags, files, topics, or tables are visible;
• sample values are present;
• timestamps are usable;
• units are understood;
• stale or impossible values are identified before mapping.

Step 7: Add one mapping

Open the connector mapping area and create one mapping rule.

At minimum, confirm:

Field	Meaning
Source path	The source tag, object, column, topic field, or file field.
Target entity	The operational target type, such as asset, point, dataset field, or work record.
Target ID	The stable ID of the target object when available.
Target field	The field DFS should write or update.
Transform expression	Unit conversion or value normalization when needed.

If AI mapping suggestions are available, review each suggestion before applying it.

Step 8: Run sync

Start the connector or run an on-demand sync.

After sync completes, open Sync History and confirm:

• status is OK or the partial/failed status is explained;
• read count is greater than zero;
• written count matches expectations;
• failed count is understood;
• sync timestamp is current.

Step 9: Check data quality

Open:

Data Integration > Data Quality

Check:

• connector quality score;
• completeness;
• timeliness;
• accuracy;
• quality trend;
• quota usage.

A first connector is ready for broader use when the source is reachable, mappings are reviewed, sync history is clean enough for the use case, and data quality issues are understood.

Step 10: Decide the next step

Need	Next step
Live operational display	Use the mapped connector data in the target FactVerse workflow.
Reusable analytics data	Create a DFS Pro dataset.
Predictive maintenance	Follow the predictive maintenance signal history recipe.
Multi-source matching	Create a fusion task.
Data quality problems	Use Data Quality and review sync logs before expanding the workflow.