Load Upbit data to DuckDB
Build a Upbit to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Upbit API base URL, auth, endpoints, and incremental loading.
Upbit is a cryptocurrency exchange that provides REST and WebSocket APIs for market data, order management, and account balance retrieval. Everything needed to build a working Upbit → 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 Upbit to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from Upbit 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 Upbit 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.
Upbit API at a glance
| Base URL | https://api.upbit.com (Korea) or region-specific URLs like https://sg-api.upbit.com, https://id-api.upbit.com, and https://th-api.upbit.com |
| Example endpoint | GET v1/market/all |
| Authentication | all authenticated requests require a Bearer token in the Authorization header — sent in the Authorization header, prefixed Bearer |
| Pagination | Page-number |
| API reference | https://global-docs.upbit.com/reference/auth |
These values come from the Upbit API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the Upbit API?
Authenticated requests require an 'Authorization' header containing a JWT bearer token signed with the HS512 algorithm, which includes the Access Key, nonce, and hash of the query parameters.
1. Get your credentials
To obtain your API credentials for the Upbit REST API, log in to your Upbit PC web account and navigate to My Profile > Open API. From there, select the desired permissions for your API key. Note that selecting 'Make Orders' or 'Withdraw' permissions requires you to register an allowlisted IP address. You must have a security level of 2 or higher to issue keys. Complete the process by entering your Fund Password and Authentication Code. Ensure you securely store your Access Key and Secret Key, as they will not be shown again.
2. Add them to .dlt/secrets.toml
[sources.upbit_source] UPBIT_ACCESS_KEY="your_access_key_here" UPBIT_SECRET_KEY="your_secret_key_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 Upbit data can I load into DuckDB?
These are the Upbit endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| market_all | v1/market/all | GET | Retrieve list of all supported trading pairs | |
| ticker | v1/ticker | GET | Retrieve current price information for markets | |
| orders_open | v1/orders/open | GET | Retrieve open order information | |
| orders_closed | v1/orders/closed | GET | Retrieve closed order information | |
| deposits | v1/deposits | GET | Retrieve deposit list information | |
| withdrawals | v1/withdrawals | GET | Retrieve withdrawal list information |
How do I load only new Upbit records?
The Upbit 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": "market_all", "endpoint": { "path": "v1/market/all", # 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 Upbit pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading orders and deposits from the Upbit API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def upbit_source(access_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://api.upbit.com (Korea) or region-specific URLs like https://sg-api.upbit.com, https://id-api.upbit.com, and https://th-api.upbit.com", "auth": {"type": "bearer", "token": access_key}, }, "resources": [ {"name": "market_all", "endpoint": {"path": "v1/market/all"}}, {"name": "orders_open", "endpoint": {"path": "v1/orders/open"}} ], } yield from rest_api_resources(config) def load_upbit_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="upbit_pipeline", destination="duckdb", dataset_name="upbit_data", ) load_info = pipeline.run(upbit_source()) print(load_info) if __name__ == "__main__": load_upbit_to_duckdb()
Run it with python upbit_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 Upbit 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("upbit_pipeline").dataset() df = data.market_all.df() print(df.head())
SQL:
SELECT * FROM upbit_data.market_all LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the Upbit 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 Upbit loads into governed, documented models.
- Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.
What other destinations can I load Upbit data to?
dlt loads into any of these — only the destination argument changes:
| Destination | Example 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.
Next steps
Was this page helpful?
Community Hub
Need more dlt context for Upbit to DuckDB?
Request dlt skills, commands, AGENT.md files, and AI-native context.