Enhance timestamp handling in Redis and CoreScouter workflows by updating to DATETIME_FORMAT_MS_WITH_TZ. Modify tests to reflect new timestamp format, ensuring consistency across data structures and improving overall datetime management.
Update sientia-dataops-library version to 1.4.3 and refactor timestamp handling in MongoDB and Redis activities to use DATETIME_FORMAT constants for improved consistency. Enhance unit tests to reflect these changes.
SIENTIAPDE-1169 Update Redis and core scouter tests to include model_tags in data structures, enhancing data organization and consistency across test cases.
Update sonar-project.properties to exclude worker.py from coverage, enhance error handling in worker.py, and add new tests for error scenarios in test_redis.py and test_mongo.py.
Refactor test cases in test_gates.py to update filter configurations, extracting policy into a dictionary format for improved clarity and consistency in data quality gate tests.
Implement store_data_package method in Redis activity for debug data storage. Update CoreScouter workflow to conditionally call store_data_package based on debug flag. Enhance tests for store_data_package functionality and error handling.
Enhance Gates and Redis activities by adding metadata parameter to apply_aggregation and notification methods. Refactor notification handling to use send_notification for improved consistency. Update tests to reflect changes in notification method calls and ensure proper functionality with new metadata integration.
Refactor Activities class to remove Kafka and Druid dependencies, simplifying initialization. Update values.yaml to set replica count to 1 for reduced resource usage. Adjust Redis activity to set TTL to None for better data retention. Remove unused Kafka and Druid activity files and their associated tests, streamlining the codebase.
Update sientia-dataops-library version to 1.2.0 in requirements.txt; refactor logging in activities to use a unified Logger instance and include metadata in log messages across various activities.
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.
