DingConnect Python API Docs | dltHub

DingConnect is a mobile top‑up and airtime distribution platform exposing a REST API for sending transfers, retrieving providers/products/reference data, balances and promotions. The REST API base URL is https://api.dingconnect.com and All requests require authentication — either OAuth2 Bearer tokens (recommended) or an API key header..

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 DingConnect data in under 10 minutes.

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What data can I load from DingConnect?

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

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How do I authenticate with the DingConnect API?

OAuth: obtain a bearer token via client_credentials at https://idp.ding.com/connect/token and include it as Authorization: Bearer . API Key: include header api_key: <your_api_key>. Optional header X-Correlation-Id may be sent for request tracing.

1. Get your credentials

1. Log in to your DingConnect account.
2. Open Account Settings → Developer (or Developer tab).
3. Create/register an OAuth client (client_id and client_secret) or generate an API Key.
4. For OAuth, use the client credentials flow to request a token from https://idp.ding.com/connect/token; for API Key, copy the issued key.

2. Add them to .dlt/secrets.toml

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[sources.ding_connect_source]
# OAuth bearer token
token = "eyJ..."
# or API key
api_key = "your_api_key_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 DingConnect 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 ding_connect_pipeline.py

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

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Pipeline ding_connect_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ding_connect_data
The duckdb destination used duckdb:/ding_connect.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 ding_connect_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 providers and products from the DingConnect 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 ding_connect_source(token (for OAuth) — also api_key supported.=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.dingconnect.com",
            "auth": {
                "type": "bearer (primary), api_key (alternative)",
                "token (for bearer), api_key (for api_key)": token (for OAuth) — also api_key supported.,
            },
        },
        "resources": [
            {"name": "providers", "endpoint": {"path": "api/V1/GetProviders", "data_selector": "Providers"}},
            {"name": "products", "endpoint": {"path": "api/V1/GetProducts", "data_selector": "Products"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ding_connect_pipeline",
        destination="duckdb",
        dataset_name="ding_connect_data",
    )
    load_info = pipeline.run(ding_connect_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("ding_connect_pipeline").dataset()
sessions_df = data.providers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ding_connect_data.providers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ding_connect_pipeline").dataset()
data.providers.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 DingConnect 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 you receive HTTP 401 or a ResultCode of 4 with ErrorCodes containing AuthenticationFailed/TokenExpired: verify your Authorization header (Bearer ) or api_key header. For OAuth request a new token from https://idp.ding.com/connect/token using client_credentials. Check token expiry and client credentials in Developer -> Account Settings.

Rate limits and Method usage limits

DingConnect enforces per-method limits (requests per 10 minutes). Common GET methods (GetProviders, GetProducts, GetRegions, GetCountries, GetProvidersStatus) have limits (typically 20‑50 requests/10min). Respect Retry-After header when receiving 429/503 and implement exponential backoff.

Pagination and paging quirks

Unbounded list endpoints use Skip and Take parameters. Take has a maximum of 100 and Skip a maximum of 500 (max 600 records retrievable). Responses include ThereAreMoreItems=true when more pages exist. Use Skip/Take to page and honor Cache-Control headers for reference data caching.

Webhooks and deferred sends

When using DeferTransfer (X-Options: DeferTransfer) SendTransfer responses return ProcessingState=Submitted and final result is sent to your notification endpoint. Verify webhook signature using X-Ding-Webhook-Signature, X-Ding-Webhook-Timestamp and public keys (headers include X-Ding-Webhook-Key-Id and algorithm).

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