Load Eway-crm data to Microsoft Fabric

eWay-CRM is a CRM platform exposing an RPC-style REST API to query and manipulate CRM data like contacts, companies, and tasks. Everything needed to build a working Eway-crm → 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 Eway-crm 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 Eway-crm 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.

---

Eway-crm API at a glance

These values come from the Eway-crm API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Eway-crm API?

Authentication is performed using OAuth 2.0. Applications must obtain client credentials (ClientID and ClientSecret) from their eWay-CRM instance to exchange for a Bearer access token, which is then passed in the Authorization header: 'Authorization: Bearer <access_token>'.

1. Get your credentials

For on-premise installations, connect to your eWay-CRM SQL Server and execute the stored procedure: EXEC dbo.eWaySP_Auth_CreateClient 'ClientName', 'RedirectURL'. Then, query the EWD_AuthClients table to retrieve your ClientID and ClientSecret (SELECT ClientID, ClientSecret FROM EWD_AuthClients WHERE ClientName = 'ClientName'). Finally, restart your eWay-CRM Web Service to register the changes. For eWay-Cloud instances, contact eWay-CRM support (support@eway-crm.com) to request ClientID and ClientSecret generation, providing your redirect URL. Ensure you have an dedicated API user created in the Admin Center (API Only license recommended).

2. Add them to .dlt/secrets.toml

Copy
[sources.eway_crm_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
instance_id = "your_instance_id"

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 Eway-crm data can I load into Microsoft Fabric?

These are the Eway-crm endpoints dlt can load into Microsoft Fabric:

---

How do I load only new Eway-crm records?

Eway-crm exposes UpdatedAt on GetContacts, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

Copy
{"name": "get_contacts", "endpoint": {
    "path": "GetContacts",
    "data_selector": "Items",
    "incremental": {"cursor_path": "UpdatedAt", "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 Eway-crm pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /API.svc and /InsecureAPI.svc from the Eway-crm API into Microsoft Fabric:

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

@dlt.source
def eway_crm_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your_instance_web_service_address}",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "get_contacts", "endpoint": {"path": "GetContacts", "data_selector": "Items"}},
            {"name": "get_companies", "endpoint": {"path": "GetCompanies", "data_selector": "Items"}}
        ],
    }
    yield from rest_api_resources(config)


def load_eway_crm_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="eway_crm_pipeline",
        destination="fabric",
        dataset_name="eway_crm_data",
    )
    load_info = pipeline.run(eway_crm_source())
    print(load_info)


if __name__ == "__main__":
    load_eway_crm_to_fabric()

Run it with uv run python eway_crm_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 Eway-crm 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("eway_crm_pipeline").dataset()
df = data.get_contacts.df()
print(df.head())

SQL:

Copy
SELECT * FROM eway_crm_data.get_contacts LIMIT 10;

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

---

How do I deploy the Eway-crm 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 Eway-crm 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 Eway-crm 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