Keyfactor EJBCA Python API Docs | dltHub

EJBCA is a certificate authority and certificate management REST API used to enroll, manage, revoke, and query certificates and CA resources. The REST API base URL is https://<HOST>:<PORT>/ejbca/ejbca-rest-api/v1 and client TLS certificate authentication (mutual TLS) or OAuth2 Bearer token (Enterprise).

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 Keyfactor EJBCA data in under 10 minutes.

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What data can I load from Keyfactor EJBCA?

Here are some of the endpoints you can load from Keyfactor EJBCA:

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How do I authenticate with the Keyfactor EJBCA API?

The API primarily requires a client TLS certificate presented by the HTTP client (mutual TLS). Enterprise deployments can alternatively accept OAuth2 access tokens; include Authorization: Bearer {token} for OAuth. Requests and responses are application/json; some download endpoints return binary PEM.

1. Get your credentials

1. For client certs: generate or obtain an Admin GUI client certificate from EJBCA admin, export key and cert (PEM/PKCS12) and configure your HTTP client (curl --cert and --key or configure client TLS in your HTTP library). 2) For OAuth: obtain an access token from your configured identity provider (OpenID Connect token endpoint) using client credentials or resource owner flow; use Authorization: Bearer {access_token} in requests.

2. Add them to .dlt/secrets.toml

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[sources.keyfactor_ejbca_source]
client_cert_path = "/path/to/client.crt"
client_key_path = "/path/to/client.key"
# OR for OAuth bearer
token = "your_oauth_access_token"

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 Keyfactor EJBCA 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 keyfactor_ejbca_pipeline.py

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

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Pipeline keyfactor_ejbca_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset keyfactor_ejbca_data
The duckdb destination used duckdb:/keyfactor_ejbca.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 keyfactor_ejbca_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 certificate/search and certificate/{issuer_dn}/{serial_number}/revocationstatus from the Keyfactor EJBCA 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 keyfactor_ejbca_source(client_cert_or_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<HOST>:<PORT>/ejbca/ejbca-rest-api/v1",
            "auth": {
                "type": "client_cert / bearer",
                "token": client_cert_or_token,
            },
        },
        "resources": [
            {"name": "certificate_search", "endpoint": {"path": "v2/certificate/search", "data_selector": "results"}},
            {"name": "certificate_revocationstatus", "endpoint": {"path": "certificate/{issuer_dn}/{serial_number}/revocationstatus"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="keyfactor_ejbca_pipeline",
        destination="duckdb",
        dataset_name="keyfactor_ejbca_data",
    )
    load_info = pipeline.run(keyfactor_ejbca_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("keyfactor_ejbca_pipeline").dataset()
sessions_df = data.certificate_search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM keyfactor_ejbca_data.certificate_search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("keyfactor_ejbca_pipeline").dataset()
data.certificate_search.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 Keyfactor EJBCA 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 (mutual TLS)

If the client certificate is missing or invalid you will see TLS-level failures (connection refused / bad certificate) or a JSON 403 error: {"error_code":403, "error_message":"Not authorized to resource ..."}. Ensure the client cert is trusted by the EJBCA server and you present the private key.

OAuth token errors

If using OAuth, missing or invalid Bearer token returns 401/403 and a JSON error message. Obtain a valid access token from the IdP configured in EJBCA and send Authorization: Bearer {token}.

Pagination quirks

Search endpoints support pagination via page_size/current_page (or cursor‑based parameters after/before/limit) and return results wrapped in a pagination cursor object when paging is used. Use page_size up to configured maximum (default limits apply).

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