Microsoft Fabric Data Activator: Event-Driven Data Alerts

Data Activator brings event-driven capabilities to Microsoft Fabric, automatically triggering actions when data conditions are met. No more polling dashboards; let the data come to you.

Understanding Data Activator

Data Activator monitors data streams and triggers actions based on conditions you define. It connects to Power BI reports, Eventstreams, and Fabric data sources.

Creating Triggers

# Data Activator configuration is primarily UI-driven, but here's how
# to structure the underlying logic programmatically

from dataclasses import dataclass
from typing import List, Dict, Callable, Any
from enum import Enum
import asyncio

class TriggerCondition(Enum):
    EQUALS = "equals"
    GREATER_THAN = "greater_than"
    LESS_THAN = "less_than"
    CHANGES_BY = "changes_by"
    ENTERS_RANGE = "enters_range"
    EXITS_RANGE = "exits_range"

class ActionType(Enum):
    EMAIL = "email"
    TEAMS_MESSAGE = "teams_message"
    POWER_AUTOMATE = "power_automate"
    CUSTOM_WEBHOOK = "webhook"

@dataclass
class TriggerRule:
    name: str
    object_type: str  # e.g., "SalesOrder", "SensorReading"
    object_id_field: str  # Field that identifies unique objects
    measure_field: str  # Field to monitor
    condition: TriggerCondition
    threshold: Any
    actions: List[Dict]

@dataclass
class DataActivatorConfig:
    name: str
    source_type: str  # "eventstream", "powerbi", "kql"
    source_connection: Dict
    triggers: List[TriggerRule]

# Example: Sales alert configuration
sales_alert = TriggerRule(
    name="Large Order Alert",
    object_type="SalesOrder",
    object_id_field="OrderId",
    measure_field="TotalAmount",
    condition=TriggerCondition.GREATER_THAN,
    threshold=50000,
    actions=[
        {
            "type": ActionType.TEAMS_MESSAGE,
            "channel": "sales-alerts",
            "template": "New large order: {{OrderId}} for ${{TotalAmount}} from {{CustomerName}}"
        },
        {
            "type": ActionType.POWER_AUTOMATE,
            "flow_id": "create-priority-ticket",
            "parameters": {"priority": "high"}
        }
    ]
)

Implementing Custom Monitoring Logic

class DataMonitor:
    """Simulate Data Activator monitoring logic."""

    def __init__(self, rules: List[TriggerRule]):
        self.rules = rules
        self.object_states: Dict[str, Dict] = {}  # Track state for change detection

    async def process_event(self, event: Dict) -> List[Dict]:
        """Process incoming event and check all trigger rules."""
        triggered_actions = []

        for rule in self.rules:
            if self._matches_object_type(event, rule):
                object_id = event.get(rule.object_id_field)
                measure_value = event.get(rule.measure_field)

                if self._evaluate_condition(rule, object_id, measure_value):
                    # Record triggered state
                    triggered_actions.extend(
                        self._prepare_actions(rule, event)
                    )

                # Update state tracking
                self._update_state(rule, object_id, measure_value)

        return triggered_actions

    def _evaluate_condition(self, rule: TriggerRule,
                           object_id: str, value: Any) -> bool:
        """Evaluate if trigger condition is met."""

        if rule.condition == TriggerCondition.GREATER_THAN:
            return value > rule.threshold

        elif rule.condition == TriggerCondition.LESS_THAN:
            return value < rule.threshold

        elif rule.condition == TriggerCondition.CHANGES_BY:
            prev = self.object_states.get(f"{rule.name}:{object_id}", {}).get("value")
            if prev is not None:
                return abs(value - prev) >= rule.threshold
            return False

        elif rule.condition == TriggerCondition.ENTERS_RANGE:
            low, high = rule.threshold
            prev = self.object_states.get(f"{rule.name}:{object_id}", {}).get("value")
            was_outside = prev is None or prev < low or prev > high
            is_inside = low <= value <= high
            return was_outside and is_inside

        return False

Data Activator transforms passive data into proactive insights. Define once, and let the system continuously monitor for conditions that matter to your business.