Atera Python API Docs | dltHub

Atera is an IT management platform that provides a REST API to read and manage resources such as agents, customers, devices, tickets, contacts, alerts and billing. The REST API base URL is https://app.atera.com/api/v3 and All requests require an account API key (API token) for authentication..

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 Atera data in under 10 minutes.

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What data can I load from Atera?

Here are some of the endpoints you can load from Atera:

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How do I authenticate with the Atera API?

Atera uses a single API key attached to your account. Supply the API key in requests via the X-API-KEY header or through the Swagger UI “Authorize” dialog. Requests must be made over HTTPS.

1. Get your credentials

1. In the Atera UI go to Admin → Data management → API.
2. Click the eye icon to reveal your API key (or use Reset my API key to generate a new one).
3. Copy the displayed API key; it will be used in the X-API-KEY request header or in the Swagger UI “Authorize” dialog.

2. Add them to .dlt/secrets.toml

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[sources.atera_source]
api_key = "your_atera_api_key_here"

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 Workbench:

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dlt 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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dlt 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 Atera 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 atera_pipeline.py

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

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Pipeline atera_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset atera_data
The duckdb destination used duckdb:/atera.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 atera_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 agents and customers from the Atera 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 atera_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.atera.com/api/v3",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "agents", "endpoint": {"path": "api/v3/agents"}},
            {"name": "customers", "endpoint": {"path": "api/v3/customers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="atera_pipeline",
        destination="duckdb",
        dataset_name="atera_data",
    )
    load_info = pipeline.run(atera_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("atera_pipeline").dataset()
sessions_df = data.agents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM atera_data.agents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("atera_pipeline").dataset()
data.agents.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 Atera 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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Troubleshooting

Authentication failures

If you receive 401 or 403 responses: verify you used the account API key from Admin → Data management → API, that you included it in the request headers (X-API-KEY) or via the Swagger Authorize dialog, and that you are calling HTTPS (https://app.atera.com). If you recently reset the API key, old keys will fail.

Rate limiting and errors

Atera docs note requests must be over HTTPS and require the API key. If you encounter 429 or other server errors, implement exponential backoff and retry. Inspect response body for error details from the API docs/Swagger UI.

Pagination

Several list endpoints in the Atera API may be paginated in the Swagger documentation (use query parameters such as page or pageSize if exposed). If you receive partial lists, consult the specific endpoint in the API docs for paging parameters and follow the provided nextPage/links or use page numbers until no more results.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

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

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

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime 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