Visma Netvisor Python API Docs | dltHub
Build a Visma-to-database pipeline in Python using dlt with automatic cursor support.
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Visma Netvisor is a cloud‑based accounting and financial management API providing programmatic access to customers, items, invoices, vouchers and other financial data. The REST API base URL is https://isvapi.netvisor.fi/ and All requests require HMAC‑SHA256 authentication headers with a MAC derived from partner and customer keys..
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 Visma Netvisor data in under 10 minutes.
What data can I load from Visma Netvisor?
Here are some of the endpoints you can load from Visma Netvisor:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| customerlist | customerlist.nv | GET | Customer | List of customers in the organisation |
| supplierlist | supplierlist.nv | GET | Supplier | List of suppliers |
| itemlist | itemlist.nv | GET | Item | List of inventory items |
| invoicelist | invoicelist.nv | GET | Invoice | List of sales invoices |
| voucherlist | voucherlist.nv | GET | Voucher | List of financial vouchers |
How do I authenticate with the Visma Netvisor API?
Authentication uses HMAC‑SHA256. Requests must include a set of X‑Netvisor‑Authentication headers; the MAC header is computed from the URI and header values using the customerKey and partnerKey, which are kept secret.
1. Get your credentials
- Log in to the Netvisor web portal as an administrator.
- Navigate to Settings → Integration → API keys (or similar) to create a new Customer API key; note the CustomerId and the generated customer key.
- Contact Netvisor support or use the Partner settings area to obtain a PartnerId and partner key for your integration.
- Store both keys securely; they will be used only for MAC calculation, not sent in the request.
2. Add them to .dlt/secrets.toml
[sources.visma_netvisor_source] customer_id = "YOUR_CUSTOMER_ID" customer_key = "YOUR_CUSTOMER_KEY" partner_id = "YOUR_PARTNER_ID" partner_key = "YOUR_PARTNER_KEY"
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:
uv venv && source .venv/bin/activate uv pip install "dlt[workspace]"
1. Install the dlt AI Workbench:
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:
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 Visma Netvisor 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:
python visma_netvisor_pipeline.py
If everything is configured correctly, you'll see output like this:
Pipeline visma_netvisor_pipeline load step completed in 0.26 seconds 1 load package(s) were loaded to destination duckdb and into dataset visma_netvisor_data The duckdb destination used duckdb:/visma_netvisor.duckdb location to store data Load package 1749667187.541553 is LOADED and contains no failed jobs
Inspect your pipeline and data:
dlt pipeline visma_netvisor_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 customerlist and invoicelist from the Visma Netvisor API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def visma_netvisor_source(customer_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://isvapi.netvisor.fi/", "auth": { "type": "hmac_sha256", "mac": customer_key, }, }, "resources": [ {"name": "customerlist", "endpoint": {"path": "customerlist.nv", "data_selector": "Customer"}}, {"name": "invoicelist", "endpoint": {"path": "invoicelist.nv", "data_selector": "Invoice"}} ], } yield from rest_api_resources(config) def get_data() -> None: pipeline = dlt.pipeline( pipeline_name="visma_netvisor_pipeline", destination="duckdb", dataset_name="visma_netvisor_data", ) load_info = pipeline.run(visma_netvisor_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):
import dlt data = dlt.pipeline("visma_netvisor_pipeline").dataset() sessions_df = data.customerlist.df() print(sessions_df.head())
SQL (DuckDB example):
SELECT * FROM visma_netvisor_data.customerlist LIMIT 10;
In a marimo or Jupyter notebook:
import dlt data = dlt.pipeline("visma_netvisor_pipeline").dataset() data.customerlist.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 Visma Netvisor data to?
dlt supports loading into any of these destinations — only the destination parameter changes:
| Destination | Example value |
|---|---|
| DuckDB (local, default) | "duckdb" |
| PostgreSQL | "postgres" |
| BigQuery | "bigquery" |
| Snowflake | "snowflake" |
| Redshift | "redshift" |
| Databricks | "databricks" |
| Filesystem (S3, GCS, Azure) | "filesystem" |
Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.
Troubleshooting
Authentication errors
- AUTHENTICATION_FAILED – Missing or incorrect authentication headers or MAC.
- INVALID_MAC – MAC calculation mismatch (wrong key, timestamp or URI encoding).
Duplicate request errors
- REQUEST_NOT_UNIQUE – Re‑using the same X‑Netvisor‑Authentication‑TransactionId for the same partner/customer.
General API errors
- Errors are returned in the
<ResponseStatus>element with a numeric code and description. - Enable
X-Netvisor-Authentication-UseHTTPResponseStatusCodes=1to receive standard HTTP 4xx/5xx status codes. - Ensure special characters in the request URI are encoded with ISO‑8859‑15 before MAC calculation.
Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.
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.
dlt ai toolkit data-exploration install dlt ai toolkit dlthub-runtime install
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