Load Microsoft Exchange Online data to Microsoft Fabric

The Exchange Online Admin API is a REST-based management surface that allows programmatic execution of specific Exchange cmdlets to replace legacy EWS scenarios for organization and mailbox management. Everything needed to build a working Microsoft Exchange Online → 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 Microsoft Exchange Online 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 Microsoft Exchange Online 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.

---

Microsoft Exchange Online API at a glance

These values come from the Microsoft Exchange Online API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Microsoft Exchange Online API?

All requests require an OAuth 2.0 access token passed in the Authorization header. Authentication is configured via Microsoft Entra ID using delegated or app-only flows with scopes such as 'https://outlook.office365.com/.default'.

1. Get your credentials

1. Navigate to the Microsoft Entra admin center (https://entra.microsoft.com/). 2. Go to 'App registrations' and select 'New registration'. 3. Once registered, navigate to 'API permissions' and click 'Add a permission'. Select 'Microsoft Graph' and choose 'Application permissions'. 4. Select the required permissions (e.g., Mail.Read, Exchange.ManageAsApp). 5. Click 'Grant admin consent' for the configured permissions. 6. Go to 'Certificates & secrets' to generate a 'Client secret' or upload a public certificate (recommended for production). 7. Record the 'Application (client) ID', 'Directory (tenant) ID', and the 'Client secret' (or certificate thumbprint) for your dlt pipeline configuration.

2. Add them to .dlt/secrets.toml

Copy
[sources.microsoft_exchange_online_source]
client_id = "your_application_client_id"
client_secret = "your_client_secret_or_certificate_path"
tenant_id = "your_directory_tenant_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 Microsoft Exchange Online data can I load into Microsoft Fabric?

These are the Microsoft Exchange Online endpoints dlt can load into Microsoft Fabric:

---

How do I load only new Microsoft Exchange Online records?

Microsoft Exchange Online exposes @odata.deltaLink on me/mailFolders/{id}/messages/delta, 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": "message_delta", "endpoint": {
    "path": "me/mailFolders/{id}/messages/delta",
    "data_selector": "value",
    "incremental": {"cursor_path": "@odata.deltaLink", "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 Microsoft Exchange Online pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading v1.0/users and v1.0/me/messages from the Microsoft Exchange Online API into Microsoft Fabric:

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

@dlt.source
def microsoft_exchange_online_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://outlook.office365.com",
            "auth": {"type": "bearer", "token": client_secret},
        },
        "resources": [
            {"name": "message_delta", "endpoint": {"path": "me/mailFolders/{id}/messages/delta", "data_selector": "value"}},
            {"name": "messages", "endpoint": {"path": "me/messages", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def load_microsoft_exchange_online_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="microsoft_exchange_online_pipeline",
        destination="fabric",
        dataset_name="microsoft_exchange_online_data",
    )
    load_info = pipeline.run(microsoft_exchange_online_source())
    print(load_info)


if __name__ == "__main__":
    load_microsoft_exchange_online_to_fabric()

Run it with python microsoft_exchange_online_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 Microsoft Exchange Online 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("microsoft_exchange_online_pipeline").dataset()
df = data.message_delta.df()
print(df.head())

SQL:

Copy
SELECT * FROM microsoft_exchange_online_data.message_delta LIMIT 10;

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

---

How do I deploy the Microsoft Exchange Online 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 Microsoft Exchange Online 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 Microsoft Exchange Online 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