Megaport Python API Docs | dltHub

The Megaport API allows users to create Virtual Cross Connects (VXC) to Google Cloud and between owned ports. The main documentation is found at https://docs.megaport.com/api/api-vxc-google/. For broader API usage, refer to https://docs.megaport.com/api/. The REST API base URL is https://api.megaport.com and All requests require a short-lived Bearer access token (OAuth2 client_credentials)..

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 Megaport data in under 10 minutes.

---

What data can I load from Megaport?

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

---

How do I authenticate with the Megaport API?

Create an API key (client ID and client secret) in the Megaport Portal, then exchange them for an access token by POSTing grant_type=client_credentials to the token URL. Include the token in an Authorization: Bearer header.

1. Get your credentials

1. Log in to your Megaport Portal (production or staging).
2. Navigate to API Keys and create a new API key, recording the client ID and client secret.
3. POST to the token URL (https://auth-m2m.megaport.com/oauth2/token) with form‑encoded grant_type=client_credentials, client ID and client secret to receive a Bearer token.
4. Use the token in the Authorization: Bearer <access_token> header for subsequent calls. Regenerate the token when it expires (max 24 h).

2. Add them to .dlt/secrets.toml

Copy
[sources.megaport_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
# optional: store a generated access token
access_token = "your_access_token_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Megaport 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:

Copy
python megaport_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline megaport_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.

---

Python pipeline example

This example loads product and product_vxc_telemetry from the Megaport API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def megaport_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.megaport.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "product", "endpoint": {"path": "v2/product"}},
            {"name": "product_vxc_telemetry", "endpoint": {"path": "v2/product/vxc/{productUid}/telemetry", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="megaport_pipeline",
        destination="duckdb",
        dataset_name="megaport_data",
    )
    load_info = pipeline.run(megaport_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.

---

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):

Copy
import dlt

data = dlt.pipeline("megaport_pipeline").dataset()
sessions_df = data.product_vxc_telemetry.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM megaport_data.product_vxc_telemetry LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("megaport_pipeline").dataset()
data.product_vxc_telemetry.df().head()

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

---

What destinations can I load Megaport 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.

---

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.

Copy
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