Imperva SDK Python API Docs | dltHub

imperva-sdk is a Python SDK that provides a client library for interacting with the Imperva SecureSphere MX Open API. The REST API base URL is https://{host}:{port} and all requests require HTTP Basic authentication with username and password.

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 Imperva SDK data in under 10 minutes.

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What data can I load from Imperva SDK?

Here are some of the endpoints you can load from Imperva SDK:

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How do I authenticate with the Imperva SDK API?

Authentication is performed via HTTP Basic authentication using a username and password supplied to the MxConnection constructor.

1. Get your credentials

1. Log in to the Imperva SecureSphere MX web console with an administrator account.
2. Navigate to Administration → Users.
3. Click Add User and fill in a username and password.
4. Ensure the new user has the API Access permission (or equivalent role).
5. Save the user; the username and password are the credentials to use with the SDK.
6. Record the host (IP or DNS) and the Open API port (default 8083) from the console under System → Network.
7. Use these values in the dlt source configuration.

2. Add them to .dlt/secrets.toml

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[sources.imperva_sdk_source]
host = "10.0.0.1"
port = 8083
username = "your_username"
password = "your_password"

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 Imperva SDK 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 imperva_sdk_pipeline.py

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

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Pipeline imperva_sdk_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset imperva_sdk_data
The duckdb destination used duckdb:/imperva_sdk.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 imperva_sdk_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 sites and web_services from the Imperva SDK 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 imperva_sdk_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{host}:{port}",
            "auth": {
                "type": "http_basic",
                "password": username,
            },
        },
        "resources": [
            {"name": "sites", "endpoint": {"path": "sites", "data_selector": "sites"}},
            {"name": "web_services", "endpoint": {"path": "web_services", "data_selector": "web_services"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="imperva_sdk_pipeline",
        destination="duckdb",
        dataset_name="imperva_sdk_data",
    )
    load_info = pipeline.run(imperva_sdk_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("imperva_sdk_pipeline").dataset()
sessions_df = data.sites.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM imperva_sdk_data.sites LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("imperva_sdk_pipeline").dataset()
data.sites.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 Imperva SDK 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 errors

• 401 Unauthorized – Returned when the username or password is incorrect. Verify the credentials in the SecureSphere console and ensure they are correctly set in secrets.toml.
• 403 Forbidden – The user exists but lacks the required API access role. Grant the appropriate permissions.

Connection errors

• ConnectionError / Timeout – Occurs if the host is unreachable or the port is wrong. Check network connectivity, firewall rules, and that the MX appliance is listening on the specified port (default 8083).

Rate limiting

• 429 Too Many Requests – The MX appliance throttles excessive API calls. Implement retry logic with exponential backoff.

Pagination quirks

• Some list endpoints return paginated results using next_page_token. The SDK provides iterator methods that handle pagination internally; ensure you iterate over the full collection rather than assuming a single response.

Common SDK exceptions

• imperva_sdk.exceptions.ApiException – Generic API error; inspect the status_code and error_message attributes for details.
• imperva_sdk.exceptions.ConnectionException – Network‑level failure; verify host/port and SSL settings.

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