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.

input_sample.json

@@ -0,0 +1,45 @@
+{
+    "topic": "fake_data",
+    "workflow_name": "scouter-fake-pipeline",
+    "schedule_name": "scouter-fake-pipeline",
+    "model_name": "fake_model",
+    "model_id": 1,
+    "trigger_laborious": false,
+    "filters": {
+        "NULL_VALUES_FILTER": {
+            "policy": "KEEP"
+        },
+        "OUT_OF_BOUNDS_FILTER": {
+            "policy": "DISCARD"
+        }
+    },
+    "schema": "fake_schema",
+    "table_name": "fake_table",
+    "retention_time": 3600,
+    "model_tags": {
+        "Temperature Sensor": {
+            "data_range": [0, 50],
+            "aggr_function": "lts"
+        },
+        "Vibration Meter": {
+            "data_range": [0, 50],
+            "aggr_function": "mdn"
+        },
+        "Pressure Gauge": {
+            "data_range": [0, 100],
+            "aggr_function": "avg"
+        },
+        "Flow Meter": {
+            "data_range": [0, 100],
+            "aggr_function": "max"
+        },
+        "Voltage Sensor": {
+            "data_range": [0, 100],
+            "aggr_function": "min"
+        },
+        "Current Sensor": {
+            "data_range": [0, 100],
+            "aggr_function": "avg"
+        }
+    }
+}