InSAR Norway Python API Docs | dltHub

InSAR Norway is a web‑based API for downloading full‑resolution InSAR data within a given bounding box. The REST API base URL is https://insar.ngu.no/insar-api and No authentication required for API requests..

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 InSAR Norway data in under 10 minutes.

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What data can I load from InSAR Norway?

Here are some of the endpoints you can load from InSAR Norway:

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How do I authenticate with the InSAR Norway API?

The API does not require authentication; requests can be made without any auth headers.

1. Get your credentials

No credentials are required; the API is publicly accessible. Simply start using the endpoints.

2. Add them to .dlt/secrets.toml

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[sources.insar_norway_source]

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 InSAR Norway 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 insar_norway_pipeline.py

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

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Pipeline insar_norway_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset insar_norway_data
The duckdb destination used duckdb:/insar_norway.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 insar_norway_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 list_datasets and query_state from the InSAR Norway 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 insar_norway_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://insar.ngu.no/insar-api",
            "auth": {
                "type": "none",
                "": ,
            },
        },
        "resources": [
            {"name": "list_datasets", "endpoint": {"path": "list-datasets"}},
            {"name": "query_state", "endpoint": {"path": "query-state?id=<query id>", "data_selector": "csv"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="insar_norway_pipeline",
        destination="duckdb",
        dataset_name="insar_norway_data",
    )
    load_info = pipeline.run(insar_norway_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("insar_norway_pipeline").dataset()
sessions_df = data.list_datasets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM insar_norway_data.list_datasets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("insar_norway_pipeline").dataset()
data.list_datasets.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 InSAR Norway 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

Query Expiration (404)

If a GET request to query-state or query-download returns a 404 response, the query has either expired or was removed by the server due to resource constraints. Clients should restart the query.

Processing Errors (state = "error")

When the state field in the query-state response is error, the messages array contains details about the failure. Retry the request after inspecting the messages.

General HTTP Errors

Non‑200 responses during query creation or polling should be retried with exponential back‑off. Persistent failures may indicate server‑side issues.

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