Load Oracle data to DuckDB

Oracle provides a diverse range of REST APIs across their cloud infrastructure, database services, and SaaS applications to programmatically manage resources and data. Everything needed to build a working Oracle → 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.

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Build your Oracle to DuckDB pipeline

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

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Oracle 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 Oracle 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.

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Oracle API at a glance

These values come from the Oracle API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Oracle API?

Oracle Cloud Infrastructure APIs require a custom Request Signature authentication mechanism where the request is signed using an RSA-SHA256 private key and transmitted via an 'Authorization' header. Other Oracle services, such as ORDS and Integration Cloud, may use standard HTTP Basic authentication or OAuth 2.0 token-based schemes depending on the specific product implementation.

1. Get your credentials

1. Log in to the Oracle Cloud Infrastructure (OCI) Console. 2. Navigate to your user profile menu in the top-right corner and select 'User Settings'. 3. In the 'Resources' list, click 'API Keys'. 4. Click 'Add API Key'. 5. Select 'Generate API Key Pair' to download the private key locally. 6. Once added, the 'Configuration File Preview' dialog will display your required credentials (Tenancy OCID, User OCID, Fingerprint, and Region). Copy these into your local ~/.oci/config file. Ensure the private key file path in the config matches where you saved the downloaded file.

2. Add them to .dlt/secrets.toml

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[sources.oracle_source]
api_key = "REPLACE_ME"

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.

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What Oracle data can I load into DuckDB?

These are the Oracle endpoints dlt can load into DuckDB:

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How do I load only new Oracle records?

Oracle exposes offset on resource_name, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "collection_resource", "endpoint": {
    "path": "resource_name",
    "data_selector": "items",
    "incremental": {"cursor_path": "offset", "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.

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What does the generated Oracle pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading instances and vcns from the Oracle API into DuckDB:

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

@dlt.source
def oracle_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Base URLs are product-specific and region-dependent, typically following the pattern 'https://<service-endpoint>/<api-path>' or 'https://<tenancy-subdomain>.<region>.oraclecloud.com'.",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "collection_resource", "endpoint": {"path": "resource_name", "data_selector": "items"}},
            {"name": "rest_enabled_sql", "endpoint": {"path": "ords/{schema}/_/sql", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def load_oracle_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="oracle_pipeline",
        destination="duckdb",
        dataset_name="oracle_data",
    )
    load_info = pipeline.run(oracle_source())
    print(load_info)


if __name__ == "__main__":
    load_oracle_to_duckdb()

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

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How do I query Oracle 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):

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import dlt

data = dlt.pipeline("oracle_pipeline").dataset()
df = data.collection_resource.df()
print(df.head())

SQL:

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SELECT * FROM oracle_data.collection_resource LIMIT 10;

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

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How do I deploy the Oracle 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 Oracle loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Oracle data to?

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

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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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack