Load BuildingConnected & Trade Tapp data to Microsoft Fabric

BuildingConnected and TradeTapp APIs allow developers to extract and manage preconstruction data, including bidding, qualification, and financial information, through Autodesk Platform Services. Everything needed to build a working BuildingConnected & Trade Tapp → Microsoft Fabric pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your BuildingConnected & Trade Tapp to Microsoft Fabric pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the BuildingConnected & Trade Tapp API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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BuildingConnected & Trade Tapp API at a glance

These values come from the BuildingConnected & Trade Tapp API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the BuildingConnected & Trade Tapp API?

The API uses Autodesk Platform Services (APS) OAuth 2.0 authentication. Requests require an Authorization header with a Bearer token obtained via a 3-legged OAuth flow.

1. Get your credentials

To obtain API credentials, you must first have an active, paid subscription to BuildingConnected or TradeTapp. Please contact your company's Account Executive or Customer Success Manager to request API access. Once enabled, navigate to your Account Settings within the BuildingConnected or TradeTapp application interface to generate your specific API credentials. Note that the APIs utilize the Autodesk Platform Services (APS) ecosystem for authentication.

2. Add them to .dlt/secrets.toml

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[sources.buildingconnected_trade_tapp_source]
aps_client_id = "your_client_id_here"
aps_client_secret = "your_client_secret_here"
# Note: These APIs typically use OAuth 2.0 3-legged authentication (Authorization Code flow) # which requires user-level login rather than static service-level API keys.

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What BuildingConnected & Trade Tapp data can I load into Microsoft Fabric?

These are the BuildingConnected & Trade Tapp endpoints dlt can load into Microsoft Fabric:

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How do I load only new BuildingConnected & Trade Tapp records?

BuildingConnected & Trade Tapp exposes cursor on projects, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "projects", "endpoint": {
    "path": "projects",
    "incremental": {"cursor_path": "cursor", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated BuildingConnected & Trade Tapp pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v2/opportunities and /v2/vendors from the BuildingConnected & Trade Tapp API into Microsoft Fabric:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def buildingconnected_trade_tapp_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://developer.api.autodesk.com/construction/buildingconnected/v2",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "projects"}},
            {"name": "bids", "endpoint": {"path": "bids"}}
        ],
    }
    yield from rest_api_resources(config)


def load_buildingconnected_trade_tapp_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="buildingconnected_trade_tapp_pipeline",
        destination="fabric",
        dataset_name="buildingconnected_trade_tapp_data",
    )
    load_info = pipeline.run(buildingconnected_trade_tapp_source())
    print(load_info)


if __name__ == "__main__":
    load_buildingconnected_trade_tapp_to_fabric()

Run it with python buildingconnected_trade_tapp_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query BuildingConnected & Trade Tapp data in Microsoft Fabric?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("buildingconnected_trade_tapp_pipeline").dataset()
df = data.projects.df()
print(df.head())

SQL:

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SELECT * FROM buildingconnected_trade_tapp_data.projects LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the BuildingConnected & Trade Tapp to Microsoft Fabric pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw BuildingConnected & Trade Tapp loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load BuildingConnected & Trade Tapp data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack