Load Adobe Analytics data to Microsoft Fabric

Adobe Analytics 2.0 APIs are a collection of REST endpoints for manipulating and integrating data from Adobe Analytics products like Analysis Workspace. Everything needed to build a working Adobe Analytics → 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 Adobe Analytics 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 Adobe Analytics 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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Adobe Analytics API at a glance

These values come from the Adobe Analytics API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Adobe Analytics API?

Requests require an 'Authorization' header with a 'Bearer' token and an 'x-api-key' header containing the client ID.

1. Get your credentials

To obtain credentials for the Adobe Analytics REST API: 1. Ensure you have System Admin or Developer rights for your Adobe organization. 2. Log in to the Adobe Developer Console (https://console.adobe.io/integrations). 3. Click Create new project. 4. Click Add API and select Adobe Analytics. 5. Choose your preferred authentication method (OAuth Server-to-Server is recommended for automated pipelines). 6. Complete the wizard to generate your Client ID (API Key) and Client Secret. 7. For Server-to-Server authentication, add the API to your project and configure the required scopes, then copy the generated credentials for use in your application.

2. Add them to .dlt/secrets.toml

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[sources.adobe_analytics_source]
adobe_client_id = "your_client_id_here"
adobe_client_secret = "your_client_secret_here"
adobe_company_id = "your_global_company_id_here"

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 Adobe Analytics data can I load into Microsoft Fabric?

These are the Adobe Analytics endpoints dlt can load into Microsoft Fabric:

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How do I load only new Adobe Analytics records?

Adobe Analytics exposes updatedDate on calculatedmetrics, 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": "calculated_metrics", "endpoint": {
    "path": "calculatedmetrics",
    "incremental": {"cursor_path": "updatedDate", "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 Adobe Analytics pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading POST /reports and GET /metrics from the Adobe Analytics API into Microsoft Fabric:

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

@dlt.source
def adobe_analytics_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://analytics.adobe.io/api/{GLOBAL_COMPANY_ID}",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "calculated_metrics", "endpoint": {"path": "calculatedmetrics"}},
            {"name": "annotations", "endpoint": {"path": "annotations"}}
        ],
    }
    yield from rest_api_resources(config)


def load_adobe_analytics_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="adobe_analytics_pipeline",
        destination="fabric",
        dataset_name="adobe_analytics_data",
    )
    load_info = pipeline.run(adobe_analytics_source())
    print(load_info)


if __name__ == "__main__":
    load_adobe_analytics_to_fabric()

Run it with python adobe_analytics_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 Adobe Analytics 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("adobe_analytics_pipeline").dataset()
df = data.calculated_metrics.df()
print(df.head())

SQL:

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SELECT * FROM adobe_analytics_data.calculated_metrics LIMIT 10;

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

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How do I deploy the Adobe Analytics 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 Adobe Analytics 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 Adobe Analytics 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