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Load Protonmail data to DuckDB

Build a Protonmail to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Protonmail API base URL, auth, endpoints, and incremental loading.

SourceProtonmailWeb Cryptography API | ProtonDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

Proton Mail is an encrypted email service that provides an internal REST API for accessing account and messaging functionality. Everything needed to build a working Protonmail → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.


Build your Protonmail to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

Prompt
Run uvx dlthub-init@latest to build a pipeline from Protonmail to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Protonmail API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.


Protonmail API at a glance

Base URLhttps://mail.proton.me/api
Example endpointGET messages
Records found atMessages
Authenticationall requests require x-pm-uid and Authorization Bearer headers — sent in the request header
Also requiredx-pm-uid, x-pm-session
PaginationPage-number

These values come from the Protonmail API documentation. Check them against the vendor's current reference before relying on them in production.


How do I authenticate with the Protonmail API?

Authentication involves exchanging credentials for a session, which provides a UID and AccessToken; these are included in requests via headers: x-pm-uid for the user ID and Authorization: Bearer for the access token.

1. Get your credentials

Proton Mail does not provide a standard static API key or developer dashboard for general REST API access to email data. Access to the Proton Mail API requires authenticating programmatically using your standard Proton account username and password via the API login endpoint (e.g., POST /auth). This exchange returns an access token (Bearer token) used for subsequent requests. Note: If you are configuring third-party SMTP/IMAP applications, navigate to Settings → All settings → IMAP/SMTP → SMTP tokens to generate a specific SMTP password.

2. Add them to .dlt/secrets.toml

[sources.protonmail_source] api_token = "your_access_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.


What Protonmail data can I load into DuckDB?

These are the Protonmail endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
messagesmessagesGETMessagesRetrieve user email messages
contactscontactsGETContactsRetrieve user contact list
labelslabelsGETLabelsRetrieve user email labels
foldersfoldersGETFoldersRetrieve user email folders
authauthPOSTAuthenticate and obtain access token

How do I load only new Protonmail records?

The Protonmail API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

{"name": "messages", "endpoint": { "path": "messages", # Replace with a field that increases on every write. "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"}, }}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.


What does the generated Protonmail pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /messages and /contacts from the Protonmail API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def protonmail_source(api_token=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://mail.proton.me/api", "auth": {"type": "bearer", "token": api_token}, }, "resources": [ {"name": "messages", "endpoint": {"path": "messages", "data_selector": "Messages"}}, {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "Contacts"}} ], } yield from rest_api_resources(config) def load_protonmail_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="protonmail_pipeline", destination="duckdb", dataset_name="protonmail_data", ) load_info = pipeline.run(protonmail_source()) print(load_info) if __name__ == "__main__": load_protonmail_to_duckdb()

Run it with python protonmail_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.


How do I query Protonmail data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

import dlt data = dlt.pipeline("protonmail_pipeline").dataset() df = data.messages.df() print(df.head())

SQL:

SELECT * FROM protonmail_data.messages LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.


How do I deploy the Protonmail to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

  • Deploy & schedule — run the pipeline as a managed job with automatic retries.
  • Monitor — observable job queues, alerting, and load metrics for every run.
  • Transform — promote raw Protonmail loads into governed, documented models.
  • Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →


What other destinations can I load Protonmail data to?

dlt loads into any of these — only the destination argument changes:

DestinationExample value
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.


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