Microsoft Azure Data Manager for Energy Python API Docs | dltHub

Azure Data Manager for Energy is a cloud-based service for managing energy data using OSDU-compliant APIs. The REST API base URL is https://{instance-name}.energy.azure.com and all requests require a Bearer token and a data-partition-id header.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Microsoft Azure Data Manager for Energy data in under 10 minutes.

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

Here are some of the endpoints you can load from Microsoft Azure Data Manager for Energy:

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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 automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI harness:

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dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlthub ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Microsoft Azure Data Manager for Energy API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python microsoft_azure_data_manager_for_energy_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline microsoft_azure_data_manager_for_energy_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset microsoft_azure_data_manager_for_energy_data
The duckdb destination used duckdb:/microsoft_azure_data_manager_for_energy.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline microsoft_azure_data_manager_for_energy_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads storage and search (specifically under /api/storage/v2/ and /api/search/v2/) from the Microsoft Azure Data Manager for Energy API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

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

SQL (DuckDB example):

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SELECT * FROM microsoft_azure_data_manager_for_energy_data.aczs LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

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

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Microsoft Azure Data Manager for Energy data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack