Load Emigo Data Source data to Microsoft Fabric

Emigo is a data management and integration system by Sagra Technology for synchronizing business data across enterprise platforms. Everything needed to build a working Emigo Data Source → Microsoft Fabric pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

---

Build your Emigo Data Source to Microsoft Fabric pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Emigo Data Source API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

---

Emigo Data Source API at a glance

These values come from the Emigo Data Source API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Emigo Data Source API?

The Emigo connector uses OAuth 2.0 authentication; users sign in with their instance credentials.

1. Get your credentials

Emigo uses OAuth 2.0 authentication for its integrations. To obtain credentials, log in to your Emigo instance through the standard user interface or the designated administrative portal provided by Sagra Technology to authorize your application or integration. There is no traditional static API key/secret dashboard for direct REST API access in the standard connector documentation; rather, it uses OAuth 2.0 flow to link your account.

2. Add them to .dlt/secrets.toml

Copy
[sources.emigo_data_source_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
refresh_token = "your_refresh_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

---

What Emigo Data Source data can I load into Microsoft Fabric?

These are the Emigo Data Source endpoints dlt can load into Microsoft Fabric:

---

How do I load only new Emigo Data Source records?

The Emigo Data Source API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

Copy
{"name": "feeds", "endpoint": {
    "path": "GetFeeds",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

---

What does the generated Emigo Data Source pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading connections and datasets (Note: As Emigo is primarily accessed via a managed Power Platform connector, direct REST API endpoint documentation is not publicly exposed; these are the standard placeholders for data pipeline configurations using managed connectors). from the Emigo Data Source API into Microsoft Fabric:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def emigo_data_source_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The official Emigo API documentation does not publicly specify a REST API base URL.",
            "auth": {"type": "api_key", "api_key": api_key},
        },
        "resources": [
            {"name": "feeds", "endpoint": {"path": "GetFeeds", "data_selector": "FeedList"}},
            {"name": "odata_items", "endpoint": {"path": "GetODataItems", "data_selector": "FeedList"}}
        ],
    }
    yield from rest_api_resources(config)


def load_emigo_data_source_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="emigo_data_source_pipeline",
        destination="fabric",
        dataset_name="emigo_data_source_data",
    )
    load_info = pipeline.run(emigo_data_source_source())
    print(load_info)


if __name__ == "__main__":
    load_emigo_data_source_to_fabric()

Run it with python emigo_data_source_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

---

How do I query Emigo Data Source data in Microsoft Fabric?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("emigo_data_source_pipeline").dataset()
df = data.feeds.df()
print(df.head())

SQL:

Copy
SELECT * FROM emigo_data_source_data.feeds LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

---

How do I deploy the Emigo Data Source to Microsoft Fabric pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Emigo Data Source loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load Emigo Data Source data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

---

Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack