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/workflow/test_fake_data.py

@@ -0,0 +1,29 @@
+from unittest.mock import AsyncMock, patch, ANY
+from pytest import fixture, mark
+from scouter.workflow.fake_data import FakeData
+from scouter.activities.faker import Faker
+
+
+@fixture
+def fake_data():
+    return FakeData()
+
+
+@mark.asyncio
+@patch('scouter.workflow.fake_data.workflow', new_callable=AsyncMock)
+async def test_fake_data_workflow(mock_workflow, fake_data):
+    mock_workflow.execute_activity_method.return_value = None
+    await fake_data.run(
+        {
+            'topic': 'test_topic'
+        }
+    )
+
+    mock_workflow.execute_activity_method.assert_called_once_with(
+        Faker.generate_and_send_data,
+        {
+            'topic': 'test_topic'
+        },
+        retry_policy=ANY,
+        start_to_close_timeout=ANY
+    )