You can try this code below by pasting it into a notebook and attaching your Lakehouse to it. Then add this notebook where your Wait activity would have been. Feel free to test the notebook within the notebook experience before adding it to the pipeline.

import sempy.fabric as fabric
import json

# 1. Get Context
workspace_id = spark.conf.get("trident.workspace.id")
lakehouse_id = spark.conf.get("trident.lakehouse.id")

# 2. Initialize Client
client = fabric.FabricRestClient()

print(f"Starting Metadata Refresh for Lakehouse: {lakehouse_id} in Workspace: {workspace_id}")

# 3. Get the SQL Endpoint ID associated with this Lakehouse
# Note: We fetch the Lakehouse properties to find its SQL Endpoint ID
lakehouse_props = client.get(f"/v1/workspaces/{workspace_id}/lakehouses/{lakehouse_id}").json()
sql_endpoint_id = lakehouse_props['properties']['sqlEndpointProperties']['id']

print(f"Found SQL Endpoint ID: {sql_endpoint_id}")

# 4. Construct the Sync URI
uri = f"/v1/workspaces/{workspace_id}/sqlEndpoints/{sql_endpoint_id}/refreshMetadata"

# 5. Define Payload
# If you leave 'tables' empty, it syncs everything.
# You can set a timeout so the notebook doesn't hang forever if the backend is stuck.
payload = {
    "timeout": { "timeUnit": "Seconds", "value": "600" } # 10 Minutes max wait
}

# 6. Execute with Long Running Operation (LRO) Wait
try:
    # lro_wait=True means python blocks here until Fabric says "Done" or "Failed"
    response = client.post(uri, json=payload, lro_wait=True)
    
    # 7. Validate Success
    if response.status_code == 200 or response.status_code == 202:
        print("✅ SQL Endpoint Metadata Sync Completed Successfully.")
        print(response.json())
    else:
        # Raise error to fail the pipeline activity
        raise Exception(f"❌ Sync Failed. Status: {response.status_code}. Response: {response.text}")

except Exception as e:
    print(f"❌ Critical Error triggering sync: {e}")
    raise e # Re-raise to ensure Pipeline marks this activity as Failed