Load Microsoft Azure Data Manager for Energy data to Microsoft Fabric

Azure Data Manager for Energy is a cloud-based service for managing energy data using OSDU-compliant APIs. Everything needed to build a working Microsoft Azure Data Manager for Energy → 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.

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Build your Microsoft Azure Data Manager for Energy 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 Azure Data Manager for Energy 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.

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Microsoft Azure Data Manager for Energy API at a glance

These values come from the Microsoft Azure Data Manager for Energy API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Microsoft Azure Data Manager for Energy API?

All requests must include an Authorization header with a Bearer token and a data-partition-id header. The Bearer token is obtained from Microsoft Entra ID (formerly Azure AD).

1. Get your credentials

Azure Data Manager for Energy (ADME) does not use static API keys. Instead, it uses Microsoft Entra ID (formerly Azure AD) OAuth 2.0 authentication. To obtain credentials: 1. Register an application in the Microsoft Entra ID portal to obtain a Client ID (Application ID) and Tenant ID. 2. Create a Client Secret under the 'Certificates & secrets' section of your registered application. 3. Use these credentials to request an access token via a POST request to https://login.microsoftonline.com//oauth2/v2.0/token with grant_type 'client_credentials', providing the scope as your ADME application ID. The returned 'access_token' is the Bearer token used in API requests.

2. Add them to .dlt/secrets.toml

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[sources.microsoft_azure_data_manager_for_energy_source]
adme_base_url = "your-instance-name.energy.azure.com"
adme_client_id = "your-client-id"
adme_client_secret = "your-client-secret"
adme_tenant_id = "your-tenant-id"
adme_data_partition_id = "your-data-partition-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.

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What Microsoft Azure Data Manager for Energy data can I load into Microsoft Fabric?

These are the Microsoft Azure Data Manager for Energy endpoints dlt can load into Microsoft Fabric:

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How do I load only new Microsoft Azure Data Manager for Energy records?

The Microsoft Azure Data Manager for Energy 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.

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{"name": "aczs", "endpoint": {
    "path": "api/acz/v1/aczs",
    # 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.

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What does the generated Microsoft Azure Data Manager for Energy pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading storage and search (specifically under /api/storage/v2/ and /api/search/v2/) from the Microsoft Azure Data Manager for Energy API into Microsoft Fabric:

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

@dlt.source
def microsoft_azure_data_manager_for_energy_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{instance-name}.energy.azure.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "aczs", "endpoint": {"path": "api/acz/v1/aczs"}},
            {"name": "reservoir_dataspaces", "endpoint": {"path": "api/reservoir-ddms/v2/dataspaces/{dataspace_name}/resources"}}
        ],
    }
    yield from rest_api_resources(config)


def load_microsoft_azure_data_manager_for_energy_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="microsoft_azure_data_manager_for_energy_pipeline",
        destination="fabric",
        dataset_name="microsoft_azure_data_manager_for_energy_data",
    )
    load_info = pipeline.run(microsoft_azure_data_manager_for_energy_source())
    print(load_info)


if __name__ == "__main__":
    load_microsoft_azure_data_manager_for_energy_to_fabric()

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

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How do I query Microsoft Azure Data Manager for Energy 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):

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import dlt

data = dlt.pipeline("microsoft_azure_data_manager_for_energy_pipeline").dataset()
df = data.aczs.df()
print(df.head())

SQL:

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SELECT * FROM microsoft_azure_data_manager_for_energy_data.aczs LIMIT 10;

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

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How do I deploy the Microsoft Azure Data Manager for Energy 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 Azure Data Manager for Energy loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Microsoft Azure Data Manager for Energy 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.

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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