Oracle Python API Docs | dltHub

Oracle Cloud Infrastructure (OCI) REST APIs is a set of HTTPS REST endpoints to manage Oracle Cloud resources (compute, networking, storage, identity, SaaS integrations) across regions. The REST API base URL is https://{service}.{region}.oraclecloud.com/<api_version> and All control-plane OCI APIs require requests to be signed using an API key (RSA key pair) tied to a user; some Oracle SaaS APIs also support OAuth2/Bearer tokens..

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 Oracle data in under 10 minutes.

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What data can I load from Oracle?

Here are some of the endpoints you can load from Oracle:

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How do I authenticate with the Oracle API?

OCI control-plane APIs use request signing: you create an API key (RSA public/private key pair), upload the public key to a user in your tenancy and include tenancy OCID, user OCID, key fingerprint and a signed request (Authorization header with signature and x-date headers). SaaS/ICCS APIs may accept OAuth2 Bearer tokens; check the specific service docs.

1. Get your credentials

1. Sign in to Oracle Cloud Console. 2) Navigate to Identity > Users and select (or create) the user that will call the API. 3) Under Resources for the user, choose 'API Keys' and add/upload a public RSA key; the console shows the key fingerprint. 4) Save the generated fingerprint and associate the user OCID and tenancy OCID (from Tenancy Details). 5) Keep the matching private RSA key locally (PEM). 6) For OAuth flows (SaaS services) register an application in the appropriate Identity/OAuth endpoint to obtain client_id/secret and exchange for a Bearer token.

2. Add them to .dlt/secrets.toml

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[sources.oracle_source]
tenancy_ocid = "ocid1.tenancy.oc1..xxxxx"
user_ocid = "ocid1.user.oc1..xxxxx"
fingerprint = "aa:bb:cc:dd:..."
private_key = "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----"
region = "us-ashburn-1"

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 Oracle 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 oracle_pipeline.py

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

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Pipeline oracle_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset oracle_data
The duckdb destination used duckdb:/oracle.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 oracle_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 instances and vcns from the Oracle 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 oracle_source(private_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{service}.{region}.oraclecloud.com/<api_version>",
            "auth": {
                "type": "api_key",
                "private_key": private_key,
            },
        },
        "resources": [
            {"name": "instances", "endpoint": {"path": "20160918/instances", "data_selector": "items"}},
            {"name": "vcns", "endpoint": {"path": "20160918/vcns", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="oracle_pipeline",
        destination="duckdb",
        dataset_name="oracle_data",
    )
    load_info = pipeline.run(oracle_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("oracle_pipeline").dataset()
sessions_df = data.instances.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM oracle_data.instances LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("oracle_pipeline").dataset()
data.instances.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 Oracle 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

Authentication failures

If requests return 401/403: verify tenancy OCID, user OCID, key fingerprint and that the correct private key is used to sign requests; confirm the request‑signing headers (Authorization, Date/x-date) and that the private key is unencrypted and in PEM format.

Pagination and missing records

Most List operations return a header opc-next-page when more pages exist; call the same List endpoint with page=<value> using that header value. Some services (e.g., Object Storage ListObjects) use a body field like nextStartWith or service‑specific pagination fields—consult the specific API page.

Rate limits / throttling

When you exceed request rate limits you receive HTTP 429 with body { "code":"TooManyRequests","message":"User-rate limit exceeded."}; implement exponential backoff (start a few seconds, up to 60s) and respect Retry-After when present.

Common error responses

OCI returns standard HTTP codes with JSON bodies like { "code": "InvalidParameter", "message": "description" }. Expect 400 (bad request), 401 (unauthenticated), 403 (unauthorized), 404 (not found), 429 (throttled), 500/503 (server errors).

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