CKE Customer Service Python API Docs | dltHub

CKE Customer Service (CKEditor Cloud Services) is a REST API platform for managing collaborative editing sessions, documents, comments, suggestions, users, access keys, secrets, insights and related resources for CKEditor Cloud Services. The REST API base URL is https://{organization_id}.cke-cs.com/api/v5/{environment_id} and HMAC request signature (request signing) with Access Key / API Secret; some management endpoints require HMAC and environment-specific secrets..

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 CKE Customer Service data in under 10 minutes.

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What data can I load from CKE Customer Service?

Here are some of the endpoints you can load from CKE Customer Service:

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How do I authenticate with the CKE Customer Service API?

CKEditor Cloud Services uses HMAC-based request signing (Request signature) for most management endpoints and additionally supports Access Keys and API Secrets for environment-level operations. Requests must include authentication headers as described in the docs (HMAC signature header and timestamp) or use Access Key/Secret pairs when interacting with management endpoints; some endpoints use API Secrets/HMAC.

1. Get your credentials

1. Sign in to the CKEditor Cloud Services / Management Panel (or On-Premises Management Panel). 2) Open the Environments / Access Keys or API Secrets section for the target environment. 3) Create a new Access Key or API Secret (Create new access key / Create new API Secret). 4) Copy the generated value (access key value or api secret value) — API secrets are shown only once. 5) Use the Access Key (id/name) and API Secret to generate HMAC-signed requests as described in the Request signature guide in the docs.

2. Add them to .dlt/secrets.toml

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[sources.cke_customer_service_source]
api_secret = "your_api_secret_here"

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 CKE Customer Service 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 cke_customer_service_pipeline.py

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

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Pipeline cke_customer_service_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cke_customer_service_data
The duckdb destination used duckdb:/cke_customer_service.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 cke_customer_service_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 storage and comments from the CKE Customer Service 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 cke_customer_service_source(api_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{organization_id}.cke-cs.com/api/v5/{environment_id}",
            "auth": {
                "type": "http_hmac",
                "api_secret": api_secret,
            },
        },
        "resources": [
            {"name": "storage", "endpoint": {"path": "storage", "data_selector": "data"}},
            {"name": "comments", "endpoint": {"path": "comments", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cke_customer_service_pipeline",
        destination="duckdb",
        dataset_name="cke_customer_service_data",
    )
    load_info = pipeline.run(cke_customer_service_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("cke_customer_service_pipeline").dataset()
sessions_df = data.storage.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cke_customer_service_data.storage LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cke_customer_service_pipeline").dataset()
data.storage.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 CKE Customer Service 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

Ensure you use the Access Key / API Secret pair and compute HMAC request signature per the Request signature guide; errors return a standardized error object with message, trace_id and status_code (401 for invalid token).

Pagination and cursors

Many list endpoints return pagination cursors (next_cursor, previous_cursor or cursor) and a data array. Use returned cursor parameters to page results; limit defaults and bounds are documented per endpoint.

Common error format

Errors include message, trace_id, status_code and often action and explanation fields. Inspect the response body for data property with error-specific details.

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