Load BQE CORE data to Microsoft Fabric

BQE CORE is a business management and accounting platform that provides REST APIs for managing company data including time entries, CRM, and HR modules. Everything needed to build a working BQE CORE → 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 BQE CORE 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 BQE CORE 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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BQE CORE API at a glance

These values come from the BQE CORE API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the BQE CORE API?

All requests require an 'Authorization: Bearer [YOUR_ACCESS_TOKEN]' header where the access token is obtained via an OAuth 2.0 flow. The base URL is dynamic and must be retrieved from the 'endpoint' field in the successful token response.

1. Get your credentials

1. Register for a developer account at the BQE CORE Developer Portal (https://api-developer.bqecore.com/webapp). 2. Once logged in, navigate to the Dashboard. 3. Add your application to the account. 4. Upon adding the app, BQE CORE will generate OAuth client credentials, specifically a Client ID and a Client Secret. 5. Copy the Client Secret immediately and store it securely, as it will not be displayed again. These credentials are required for the OAuth 2.0 authorization flow to obtain access tokens.

2. Add them to .dlt/secrets.toml

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[sources.bqe_core_source]
access_token = "REPLACE_ME"

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 BQE CORE data can I load into Microsoft Fabric?

These are the BQE CORE endpoints dlt can load into Microsoft Fabric:

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How do I load only new BQE CORE records?

BQE CORE exposes lastUpdated on bill, 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": "bill", "endpoint": {
    "path": "bill",
    "incremental": {"cursor_path": "lastUpdated", "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 BQE CORE pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading token and authorize from the BQE CORE API into Microsoft Fabric:

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

@dlt.source
def bqe_core_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://[dynamic-data-center-host] (retrieved from token response)",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "bill", "endpoint": {"path": "bill"}},
            {"name": "invoice", "endpoint": {"path": "invoice"}}
        ],
    }
    yield from rest_api_resources(config)


def load_bqe_core_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bqe_core_pipeline",
        destination="fabric",
        dataset_name="bqe_core_data",
    )
    load_info = pipeline.run(bqe_core_source())
    print(load_info)


if __name__ == "__main__":
    load_bqe_core_to_fabric()

Run it with python bqe_core_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 BQE CORE 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("bqe_core_pipeline").dataset()
df = data.bill.df()
print(df.head())

SQL:

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SELECT * FROM bqe_core_data.bill LIMIT 10;

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

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How do I deploy the BQE CORE 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 BQE CORE 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 BQE CORE 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