Embed - save copy report - data change impact

Hi rdumont ,

Power BI Embedded users face report breakage when table or column names change in the semantic model. To prevent this, schema changes must be detected in advance and reports updated automatically.

Using the Power BI REST API, dataset schema can be extracted and compared with previous versions to detect changes. The Get Dataset Tables API fetches table and column structures:

GET https://api.powerbi.com/v1.0/myorg/datasets/{datasetId}/tables
Authorization: Bearer {access_token}

For deeper analysis in Premium/PPU workspaces, XMLA Endpoint DMV queries provide metadata:

SELECT * FROM $System.TMSCHEMA_COLUMNS

Once changes are detected, user reports can be updated by exporting the report JSON, replacing outdated references, and re-importing it:

GET https://api.powerbi.com/v1.0/myorg/reports/{reportId}/export

POST https://api.powerbi.com/v1.0/myorg/reports/import
Authorization: Bearer {access_token}
Content-Type: application/json
{
    "name": "Updated Report",
    "file": "{modified_json_report}"
}

A more seamless approach is using SQL views to maintain column aliases or creating duplicate calculated columns with old names before renaming. Automating schema checks and report updates ensures minimal user impact. Let me know if you’d like a script to automate this.

Best regards,

Hi rdumont ,

The best approach to prevent Power BI Embedded reports from breaking due to schema changes is to automate schema detection and report updates. Using the Power BI REST API, you can extract dataset schema and compare it with previous versions to detect renamed or deleted tables and columns. This can be done using the API endpoint:

GET https://api.powerbi.com/v1.0/myorg/datasets/{datasetId}/tables
Authorization: Bearer {access_token}

For Premium or PPU workspaces, XMLA Endpoint DMV queries provide deeper metadata insights:

SELECT * FROM $System.TMSCHEMA_COLUMNS

Once schema changes are detected, user reports can be updated by exporting the report JSON, replacing outdated references, and re-importing it. The export and import operations are performed using the following API endpoints:

GET https://api.powerbi.com/v1.0/myorg/reports/{reportId}/export
Authorization: Bearer {access_token}

POST https://api.powerbi.com/v1.0/myorg/reports/import
Authorization: Bearer {access_token}
Content-Type: application/json
{
    "name": "Updated Report",
    "file": "{modified_json_report}"
}

A Python script can automate this process by retrieving the dataset schema, storing previous versions, detecting changes, and updating reports accordingly. The script starts by authenticating with Power BI and fetching the dataset schema:

import requests
import json

TENANT_ID = "your-tenant-id"
CLIENT_ID = "your-client-id"
CLIENT_SECRET = "your-client-secret"
WORKSPACE_ID = "your-workspace-id"
DATASET_ID = "your-dataset-id"

AUTH_URL = f"https://login.microsoftonline.com/{TENANT_ID}/oauth2/v2.0/token"

def get_access_token():
    data = {
        "grant_type": "client_credentials",
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
        "scope": "https://graph.microsoft.com/.default",
        "resource": "https://analysis.windows.net/powerbi/api"
    }
    headers = {"Content-Type": "application/x-www-form-urlencoded"}
    response = requests.post(AUTH_URL, data=data, headers=headers)
    return response.json().get("access_token")

def get_dataset_schema():
    headers = {"Authorization": f"Bearer {get_access_token()}"}
    response = requests.get(f"https://api.powerbi.com/v1.0/myorg/datasets/{DATASET_ID}/tables", headers=headers)
    return response.json()

This schema is compared with a stored previous version to detect renamed tables and columns. If changes are detected, the script exports affected reports, modifies table/column references, and re-imports the updated reports:

def export_report(report_id):
    headers = {"Authorization": f"Bearer {get_access_token()}"}
    response = requests.get(f"https://api.powerbi.com/v1.0/myorg/reports/{report_id}/export", headers=headers)
    return response.json()

def update_report_json(report_json, changes):
    updated_json = json.dumps(report_json)
    for old_table, new_table in changes["renamed_tables"].items():
        updated_json = updated_json.replace(old_table, new_table)
    for table, col_map in changes["renamed_columns"].items():
        for old_col, new_col in col_map.items():
            updated_json = updated_json.replace(old_col, new_col)
    return json.loads(updated_json)

def import_report(updated_json, report_name):
    headers = {
        "Authorization": f"Bearer {get_access_token()}",
        "Content-Type": "application/json"
    }
    data = {"name": report_name, "file": json.dumps(updated_json)}
    response = requests.post("https://api.powerbi.com/v1.0/myorg/reports/import", headers=headers, json=data)
    return response.status_code

By automating schema monitoring and report updates, user impact can be minimized when unavoidable table or column name changes occur. Additionally, using SQL views or calculated columns with old names can help maintain stability. Let me know if you need further refinements or additional features like email notifications or Power Automate integration.

Best regards,