ttd-data

Developer-friendly & type-safe Python SDK specifically catered to leverage ttd-data API.

Summary

TTD Data API: Python SDK for The Trade Desk Data API. Provides operations for ingesting advertiser data, third-party data, and offline conversions, as well as handling data subject deletion and opt-out requests.

For more information, see the official API documentation:

• Advertiser targeting data (1PD)
• Third-party targeting data (3PD)
• Offline conversions (CAPI)

Deletions and opt-outs:

• Advertiser
• Third party
• Merchant

Table of Contents

• ttd-data
  • SDK Installation
  • IDE Support
  • SDK Example Usage
  • Available Resources and Operations
  • Retries
  • Error Handling
  • Custom HTTP Client
  • Resource Management
  • Debugging
• Development
  • Maturity
  • Contributions

SDK Installation

[!NOTE] Python version upgrade policy

Once a Python version reaches its official end of life date, a 3-month grace period is provided for users to upgrade. Following this grace period, the minimum python version supported in the SDK will be updated.

The SDK can be installed with uv, pip, or poetry package managers.

uv

uv is a fast Python package installer and resolver, designed as a drop-in replacement for pip and pip-tools. It's recommended for its speed and modern Python tooling capabilities.

uv add ttd-data

PIP

PIP is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.

pip install ttd-data

Poetry

Poetry is a modern tool that simplifies dependency management and package publishing by using a single pyproject.toml file to handle project metadata and dependencies.

poetry add ttd-data

Shell and script usage with uv

You can use this SDK in a Python shell with uv and the uvx command that comes with it like so:

uvx --from ttd-data python

It's also possible to write a standalone Python script without needing to set up a whole project like so:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "ttd-data",
# ]
# ///

from ttd_data import DataClient

sdk = DataClient(
  # SDK arguments
)

# Rest of script here...

Once that is saved to a file, you can run it with uv run script.py where script.py can be replaced with the actual file name.

IDE Support

PyCharm

Generally, the SDK will work well with most IDEs out of the box. However, when using PyCharm, you can enjoy much better integration with Pydantic by installing an additional plugin.

• PyCharm Pydantic Plugin

SDK Example Usage

1. Advertiser targeting Data (1PD)

from ttd_data import DataClient, models

client = DataClient(ttd_auth=TTD_AUTH_TOKEN)
response = client.advertiser.ingest_advertiser_data(
    advertiser_id=ADVERTISER_ID,
    items=[
        models.AdvertiserDataItem(
            tdid="<TDID>",
            data=[
                models.AdvertiserData(name="loyalty_members"),
            ],
        )
    ],
)

2. Third Party Targeting Data (3PD)

from ttd_data import DataClient, models

client = DataClient(ttd_auth=TTD_AUTH_TOKEN)
response = client.third_party.ingest_third_party_data(
    data_provider_id=DATA_PROVIDER_ID,
    items=[
        models.ThirdPartyDataItem(
            tdid="<TDID>",
            data=[
                models.ThirdPartyData(name="in_market_auto"),
            ],
        )
    ],
)

3. Offline Conversions Data (CAPI)

from datetime import datetime, timezone
from ttd_data import DataClient, UserIdType, models

client = DataClient(ttd_auth=TTD_AUTH_TOKEN)
response = client.offline_conversion.ingest_offline_conversion_data(
    data_provider_id=DATA_PROVIDER_ID,
    items=[
        # Pre-resolved TDID
        models.OfflineConversionDataItem(
            tracking_tag_id=TRACKING_TAG_ID,
            timestamp_utc=datetime.now(timezone.utc),
            tdid="<TDID>",
        ),
        # Multiple identifiers via UserIdArray
        models.OfflineConversionDataItem(
            tracking_tag_id=TRACKING_TAG_ID,
            timestamp_utc=datetime.now(timezone.utc),
            user_id_array=[
                [UserIdType.TDID, "<TDID>"],
                [UserIdType.UID2, "<UID2>"],
            ],
        ),
    ],
    # Required whenever any item uses user_id_array
    user_id_array_metadata_format=["type", "id"],
)

4. Optouts and Deletion - Advertiser - Data Subject Request

from ttd_data import DataClient, models

client = DataClient(ttd_auth=TTD_AUTH_TOKEN)
response = client.deletion_opt_out.data_subject_request_advertiser_data(
    advertiser_id=ADVERTISER_ID,
    request_type=models.PartnerDsrRequestType.DELETION,
    items=[
        models.PartnerDsrDataItem(tdid="<TDID>"),
        models.PartnerDsrDataItem(daid="<DAID>"),
        models.PartnerDsrDataItem(euid="<EUID>"),
    ],
)

5. Optouts and Deletion - Data Provider - Data Subject Request

from ttd_data import DataClient, models

client = DataClient(ttd_auth=TTD_AUTH_TOKEN)
response = client.deletion_opt_out.data_subject_request_third_party_data(
    data_provider_id=DATA_PROVIDER_ID,
    request_type=models.PartnerDsrRequestType.OPT_OUT,
    items=[
        models.PartnerDsrDataItem(tdid="<TDID>"),
        models.PartnerDsrDataItem(ramp_id="<RAMP_ID>"),
    ],
)

6. Optouts and Deletion - Merchant - Data Subject Request

from ttd_data import DataClient, models

client = DataClient(ttd_auth=TTD_AUTH_TOKEN)
response = client.deletion_opt_out.data_subject_request_merchant_data(
    merchant_id=MERCHANT_ID,
    request_type=models.PartnerDsrRequestType.DELETION,
    items=[
        models.PartnerDsrDataItem(tdid="<TDID>"),
    ],
)

7. UID2 Identity Mapping

Supply a UID2Config to resolve raw PII (email / phone) to UID2 before ingest. Per-item mapping failures (opted-out or unmapped identifiers) appear in failed_lines with ErrorCode = "Uid2Error". A complete UID2 service failure raises UID2ServiceError.

from ttd_data import DataClient, IdentityScope, UID2Config, UID2ServiceError
from ttd_data.models import AdvertiserData, AdvertiserDataItem

uid2_config = UID2Config(
    base_url="<UID2_BASE_URL>",
    api_key="<UID2_API_KEY>",
    client_secret="<UID2_CLIENT_SECRET>",
    identity_scope=IdentityScope.UID2,
)

try:
    client = DataClient(
        ttd_auth=TTD_AUTH_TOKEN,
        uid2_config=uid2_config,
        server_url="<TTD_DATA_SERVER_URL>",
    )
    response = client.advertiser.ingest_advertiser_data(
        advertiser_id=ADVERTISER_ID,
        items=[
            # Raw email — resolved to UID2 before ingest
            AdvertiserDataItem(
                data=[AdvertiserData(name="loyalty_members")],
                email="user@example.com",
            ),
            # Pre-hashed email (SHA-256, base64-encoded)
            AdvertiserDataItem(
                data=[AdvertiserData(name="loyalty_members")],
                hashed_email="<SHA256_BASE64>",
            ),
            # Raw phone (E.164 format) — resolved to UID2 before ingest
            AdvertiserDataItem(
                data=[AdvertiserData(name="loyalty_members")],
                phone="+15555550123",
            ),
            # Pre-hashed phone (SHA-256 of the normalized E.164 string, base64-encoded)
            AdvertiserDataItem(
                data=[AdvertiserData(name="loyalty_members")],
                hashed_phone="<SHA256_BASE64>",
            ),
            # Pre-resolved TDID — no UID2 work needed
            AdvertiserDataItem(
                data=[AdvertiserData(name="loyalty_members")],
                tdid="<TDID>",
            ),
        ],
    )

    # Check for per-item mapping failures
    server_response = response.advertiser_data_server_response
    if server_response and server_response.failed_lines:
        for line in server_response.failed_lines:
            print(f"Item {line.item_number} failed: {line.error_code} — {line.message}")

except UID2ServiceError as e:
    # The UID2 identity-map service itself failed — no items were sent
    print(f"UID2 service error: {e}")

8. Async usage

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from ttd_data import DataClient, models

async def main():
    data_client = DataClient(ttd_auth=TTD_AUTH_TOKEN)
    response = await data_client.advertiser.ingest_advertiser_data_async(
        advertiser_id=ADVERTISER_ID,
        items=[
            models.AdvertiserDataItem(
                tdid="<TDID>",
                data=[
                    models.AdvertiserData(name="loyalty_members"),
                ],
            )
        ],
    )

    # Handle response
    print(response.advertiser_data_server_response)

asyncio.run(main())

Available Resources and Operations

Retries

Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.

To change the default retry strategy for a single API call, simply provide a RetryConfig object to the call:

from ttd_data import DataClient
from ttd_data.utils import BackoffStrategy, RetryConfig


data_client = DataClient(ttd_auth="<value>")

res = data_client.advertiser.ingest_advertiser_data(advertiser_id="<id>",
    retries=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))

assert res.advertiser_data_server_response is not None

# Handle response
print(res.advertiser_data_server_response)

If you'd like to override the default retry strategy for all operations that support retries, you can use the retry_config optional parameter when initializing the SDK:

from ttd_data import DataClient
from ttd_data.utils import BackoffStrategy, RetryConfig


data_client = DataClient(
    ttd_auth="<value>",
    retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
)

res = data_client.advertiser.ingest_advertiser_data(advertiser_id="<id>")

assert res.advertiser_data_server_response is not None

# Handle response
print(res.advertiser_data_server_response)

Error Handling

DataError is the base class for all HTTP error responses. It has the following properties:

Example

from ttd_data import DataClient, errors


data_client = DataClient(ttd_auth="<value>")
res = None
try:

    res = data_client.advertiser.ingest_advertiser_data(advertiser_id="<id>")

    assert res.advertiser_data_server_response is not None

    # Handle response
    print(res.advertiser_data_server_response)


except errors.DataError as e:
    # The base class for HTTP error responses
    print(e.message)
    print(e.status_code)
    print(e.body)
    print(e.headers)
    print(e.raw_response)

    # Depending on the method different errors may be thrown
    if isinstance(e, errors.AdvertiserDataServerResponseError):
        print(e.data.failed_lines)  # OptionalNullable[List[models.AdvertiserDataServerResponseLine]]
        print(e.data.http_meta)  # models.HTTPMetadata

Error Classes

Primary error:

• DataError: The base class for HTTP error responses.

* Check the method documentation to see if the error is applicable.

Custom HTTP Client

The Python SDK makes API calls using the httpx HTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with your own HTTP client instance. Depending on whether you are using the sync or async version of the SDK, you can pass an instance of HttpClient or AsyncHttpClient respectively, which are Protocol's ensuring that the client has the necessary methods to make API calls. This allows you to wrap the client with your own custom logic, such as adding custom headers, logging, or error handling, or you can just pass an instance of httpx.Client or httpx.AsyncClient directly.

For example, you could specify a header for every request that this sdk makes as follows:

from ttd_data import DataClient
import httpx

http_client = httpx.Client(headers={"x-custom-header": "someValue"})
s = DataClient(client=http_client)

or you could wrap the client with your own custom logic:

from ttd_data import DataClient
from ttd_data.httpclient import AsyncHttpClient
from typing import Any, Optional, Union
import httpx

class CustomClient(AsyncHttpClient):
    client: AsyncHttpClient

    def __init__(self, client: AsyncHttpClient):
        self.client = client

    async def send(
        self,
        request: httpx.Request,
        *,
        stream: bool = False,
        auth: Union[
            httpx._types.AuthTypes, httpx._client.UseClientDefault, None
        ] = httpx.USE_CLIENT_DEFAULT,
        follow_redirects: Union[
            bool, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
    ) -> httpx.Response:
        request.headers["Client-Level-Header"] = "added by client"

        return await self.client.send(
            request, stream=stream, auth=auth, follow_redirects=follow_redirects
        )

    def build_request(
        self,
        method: str,
        url: httpx._types.URLTypes,
        *,
        content: Optional[httpx._types.RequestContent] = None,
        data: Optional[httpx._types.RequestData] = None,
        files: Optional[httpx._types.RequestFiles] = None,
        json: Optional[Any] = None,
        params: Optional[httpx._types.QueryParamTypes] = None,
        headers: Optional[httpx._types.HeaderTypes] = None,
        cookies: Optional[httpx._types.CookieTypes] = None,
        timeout: Union[
            httpx._types.TimeoutTypes, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
        extensions: Optional[httpx._types.RequestExtensions] = None,
    ) -> httpx.Request:
        return self.client.build_request(
            method,
            url,
            content=content,
            data=data,
            files=files,
            json=json,
            params=params,
            headers=headers,
            cookies=cookies,
            timeout=timeout,
            extensions=extensions,
        )

s = DataClient(async_client=CustomClient(httpx.AsyncClient()))

Resource Management

DataClient registers a finalizer that closes the underlying sync and async HTTPX clients when the instance is garbage collected. This closes HTTP connections, releases memory and frees up other resources held by the SDK. In short-lived Python programs and notebooks that make a few SDK method calls, resource management may not be a concern. However, in longer-lived programs, it is beneficial to create a single SDK instance and reuse it across the application.

from ttd_data import DataClient

client = DataClient(ttd_auth="<TTD_AUTH_TOKEN>")


def main():
    # Reuse the single client for every request
    ...


# The same instance also serves async calls:
async def amain():
    ...

Debugging

You can setup your SDK to emit debug logs for SDK requests and responses.

You can pass your own logger class directly into your SDK.

from ttd_data import DataClient
import logging

logging.basicConfig(level=logging.DEBUG)
s = DataClient(server_url="https://example.com", debug_logger=logging.getLogger("ttd_data"))

You can also enable a default debug logger by setting an environment variable TTD_DATA_DEBUG to true.

Development

Maturity

This SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally looking for the latest version.

Contributions

While we value open-source contributions to this SDK, this library is generated programmatically. Any manual changes added to internal files will be overwritten on the next generation. We look forward to hearing your feedback. Feel free to open a PR or an issue with a proof of concept and we'll do our best to include it in a future release.

SDK Created by Speakeasy