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_faker.py

@@ -0,0 +1,108 @@
+import pytest
+from unittest.mock import MagicMock, patch, call
+from scouter.activities.faker import Faker
+from logging import Logger
+from sientia_do.notifications.handlers import NotificationHandler
+
+
+@pytest.fixture
+def mock_kafka_producer():
+    with patch('scouter.activities.faker.KafkaProducer') as mock:
+        producer = MagicMock()
+        mock.return_value = producer
+        yield producer
+
+
+@pytest.fixture
+def mock_datetime():
+    with patch('scouter.activities.faker.datetime') as mock_dt:
+        mock_dt.now.return_value.strftime.return_value = '2025-05-14 14:54:24'
+        yield mock_dt
+
+
+@pytest.fixture
+def faker_instance(mock_kafka_producer):
+    logger = MagicMock(spec=Logger)
+    notification_handler = MagicMock(spec=NotificationHandler)
+    return Faker(
+        bootstrap_servers='localhost:9092',
+        logger=logger,
+        notification_handler=notification_handler
+    )
+
+
+@pytest.mark.asyncio
+async def test_faker_init(faker_instance, mock_kafka_producer):
+    """Test Faker initialization with correct parameters"""
+    assert faker_instance.producer is not None
+    assert len(faker_instance.tags) == 6
+
+
+@pytest.mark.asyncio
+async def test_generate_and_send_data_default_count(faker_instance, mock_kafka_producer, mock_datetime):
+    """Test generating data with default message count"""
+    # Mock random.choice to control the output
+    with patch('random.choice') as mock_choice, \
+            patch('random.uniform', return_value=42.5), \
+            patch('random.randint', return_value=3):
+
+        # Setup mock for tag and name selection
+        mock_choice.side_effect = [
+            'ns=1;i=1001',
+            'ns=1;i=1002',
+            'ns=1;i=1003'
+        ]
+
+        # Call the method
+        await faker_instance.generate_and_send_data({'topic': 'test_topic'})
+
+        # Verify the producer was called 3 times (default count)
+        assert mock_kafka_producer.send.call_count == 3
+        mock_kafka_producer.flush.assert_called_once()
+
+        # Verify the message format
+        expected_data = {
+            'tag': 'ns=1;i=1001',
+            'name': 'Temperature Sensor',
+            'timestamp': '2025-05-14 14:54:24',
+            'value': 42.5
+        }
+        mock_kafka_producer.send.assert_any_call(
+            'test_topic', value=expected_data)
+
+
+@pytest.mark.asyncio
+async def test_generate_and_send_data_custom_count(faker_instance, mock_kafka_producer):
+    """Test generating data with custom message count"""
+    # Call the method with custom count
+    await faker_instance.generate_and_send_data({
+        'topic': 'test_topic',
+        'num_messages': 2
+    })
+
+    # Verify the producer was called 2 times
+    assert mock_kafka_producer.send.call_count == 2
+    mock_kafka_producer.flush.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_generate_and_send_data_no_topic(faker_instance):
+    """Test that ValueError is raised when no topic is provided"""
+    with pytest.raises(ValueError, match="Topic must be specified in input_data"):
+        await faker_instance.generate_and_send_data({})
+
+
+@pytest.mark.asyncio
+async def test_generate_and_send_data_random_values(faker_instance, mock_kafka_producer):
+    """Test that random values are within expected ranges"""
+    # Call the method
+    await faker_instance.generate_and_send_data({'topic': 'test_topic'})
+
+    # Get the call arguments
+    call_args = mock_kafka_producer.send.call_args[1]['value']
+
+    # Verify the data structure
+    assert 'tag' in call_args
+    assert call_args['tag'] in faker_instance.tags
+    assert 'value' in call_args
+    assert 0 <= call_args['value'] <= 100