SIENTIAPDE-1005

Implement workflows for fake data generation, scouter processing, and core scouter operations

- Added `FakeData` workflow to generate random data and send it to a Kafka topic.
- Implemented `Scouter` workflow to load data from Kafka and trigger the core scouter workflow.
- Created `CoreScouter` workflow to process data through quality gates, aggregation, and export to PostgreSQL.
- Developed comprehensive unit tests for activities and workflows, ensuring proper functionality and error handling.
- Enhanced Redis and Postgres activities with robust testing for data handling and error notifications.
- Introduced quality filters for data validation and implemented tests to verify their functionality.

tests/activities/test_kafka.py

@@ -1,5 +1,5 @@
 from unittest.mock import MagicMock, patch, ANY
-from pytest import fixture
+from pytest import fixture, mark
 from pandas import DataFrame
 from scouter.activities.kafka import Kafka
 
@@ -38,7 +38,8 @@ def test___init__(kafka_consumer):
     )
 
 
-def test_load_from_kafka(kafka):
+@mark.asyncio
+async def test_load_from_kafka(kafka):
     input_data = {"topic": "test-topic"}
 
     data = [
@@ -55,7 +56,7 @@ def test_load_from_kafka(kafka):
 
     expected = DataFrame([d.value for d in data[0][1]]).to_dict()
 
-    result = kafka.load_from_kafka(input_data)
+    result = await kafka.load_from_kafka(input_data)
 
     assert result == expected