- (Backend) Refore code to improved logging and error handling
- (Backend) Improved tests
- (Frontend) Refector code to improve Analyzer handling
- (Frontend) improve tests and messages format to handle new backend enhancements.

Refactor in Analytics, Telemetry, and KpiManager Services and Manifest Files to Reflect Generic DB Changes.

- Improvements made to `tfs.sh` script.
- Added `CRDB_DATABASE` environment variable to all monitoring service manifest files and the context manifest file.
- Removed the DB engine file from all monitoring services and moved it to `common.tools.database`.
- Each `<service_name>_DB.py` will now retrieve the database name from the environment variable.

- Changed `StartAnalyzer` return type to `AnalyzerId`.
- Added `APScheduler` to requirements.
- Updated `AnalyticsBackend` Dockerfile to set `JAVA_HOME`.
- Added `Alarms` topic in the Kafka class.

- Removed `AnalyzerAlarms` from the proto file.
- Moved Alarms functionality from Analytics Frontend to KpiValueAPI.

- Added `AnalyzerAlarms` message type in the Proto file.
- Changed the return type of `StartAnalyzer` to `AnalyzerAlarms`.

- Added `KpiValue` message type in `AnalyticsFrontend.proto`.
- Changed `StartAnalyzer` response type from message to stream.
- Added `StartResponseListener()` call and response handler in the `StartAnalyzer` method.
- Implemented `ConvertValueToKpiValueType` to handle `OneOf` type conversion.
- Added tests to handle stream responses.

Backend:
- Updated the `StartSparkStreamer` function call to send `Analyzer_uuid` as the key for messages produced on the Kafka topic.
- Updated the `SparkStream` definition to receive the key and added the key to the `streamwriter` object.

Frontend:
- Integrated APScheduler to manage `StreamListener`.
- Added `ResponseListener` to consume messages from the `analytics_response_topic` and process them.
- Added APScheduler to manage StreamListener.
- Added "ResponseListener" to consumer messages from analytics response_topic and process it.

Proto:
- Added `Duration_s` field to the proto file.

Frontend:
- Added `SelectAnalyzer` logic.
- Improved message formatting in the `create_analyzer_filter()` function.
- Added a test case: `test_SelectAnalytics`.

Backend:
- Renamed the `RunSparkStreamer` method to `StartSparkStreamer`.
- Updated the `StartRequestListener` method to return `(thread, stop_event)`.
- Added a `StopRequestListener` method to stop the listener.

Database:
- Added the `select_with_filter` method with actual logic implementation.
- Updated the `ConvertRowToAnalyzer` method to correctly read the `operation_mode` ENUM value.

**Backend:**
- Added a dictionary to manage running analyzers.
- Implemented logic to manage running analyzers.
- Added the `TerminateAnalyzerBackend` method to handle analyzer termination.

**Frontend:**
- Modified and invoked the `PublishStopRequestOnKafka` method.

- Thresholds, window_size, window_slider is added in Frontend and Backend.

- Added Kafka Request Listener to listen for requests from the Analytics Frontend.
- Updated `SparkStreamer.py` to consume and process streams from VALUE topics, and further filter rows based on KPIs in the `input_kpis` list.
- Updated the frontend TOPIC from `VALUE` to `ANALYTICS_REQUEST`.
- Updated messages to keep the KPI UUID consistent with the one generated by the Telemetry Backend service.

- Created the Analytic Engine, Model, and DB files.
- Added the DB connection.
- Added `add_row_to_db` in `StartCollector`.
- Added `delete_db_row_by_id` in `StopCollector`.
- Improved message formatting.
- Added a DB test file.

- Actul logic is added in StartAnalyzer method.
- Actual logic is added in StopAnalyzer method.
- Improvements in message formats.

- Added the Analytics Frontend client and service with no logic implemented yet.
- Added enums and ports for the Analytics frontend and backend in the constants.
- Added test files and messages.
- Added test execution scripts.