Implement tag value registration in Gates activity and update related tests. Enhanced the write_metrics method to process and register tag values, improving metrics tracking. Adjusted tests to validate new functionality.
Update Redis and CoreScouter to incorporate 'fill_missing_tags' parameter in data processing. Adjusted related tests to ensure proper handling of missing tags, enhancing overall functionality and consistency across workflows.
SIENTIAPDE-1318: Add 'fill_missing_tags' parameter to CoreScouter for enhanced tag handling in data processing. This update allows for the specification of missing tags during workflow execution.
Update .gitignore and refactor metrics.py, activities.py, and gates.py for improved clarity and consistency. Added coverage.xml and cache directories to .gitignore. Standardized string formatting and parameter handling in metrics and activities classes, enhancing code readability. Removed the deprecated faker.py file and adjusted related tests accordingly.
Remove deprecated files and configurations, including .env, Dockerfile, docker-compose.yml, and client-schedule.py. Update README.md to reflect new architecture and features, enhancing clarity on system capabilities and workflows. Adjust values.yaml for image tag and replica count, and improve code documentation across various modules for better maintainability.
Refactor timestamp handling in CoreScouter workflow by updating to DATETIME_FORMAT_WITH_TZ, ensuring consistency with recent changes in datetime management.
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 requirements and core scouter workflow: comment out old dataops library dependency, change default namespace in worker.py, and enhance timestamp handling in core_scouter.py with DATETIME_FORMAT.
Update dependencies and improve observability by changing logger imports. Bump sientia-dataops-library version to 1.4.1 and update image tag to 0.4.2 in values.yaml.
Update image tag to 0.4.0 in values.yaml, modify GITHUB_BRANCH for SIENTIAPDE-1169, and enhance Redis activity to handle model tags for improved data retention.
SIENTIAPDE-1174 Add pod_id initialization in Activities class and update Scouter to reference it for improved consistency in environment variable handling.
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.
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.
Refactor Scouter workflow to update data loading method, changing from load_latest_druid_data to load_latest_data. Adjusted parameters for consistency in collection naming and improved clarity in data retrieval process.
Integrate Druid activity into the Activities class, adding support for Druid configuration and initialization. Update worker and connectors configuration to accommodate Druid, enhancing data processing capabilities.
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.
Update environment configuration and refactor activity imports
- Changed Kafka, Redis, and Temporal host configurations to use localhost.
- Updated the version reference for the sientia-dataops-library in requirements.txt.
- Refactored import paths for activities to align with new module structure.
- Removed unused base.py and postgres.py files.
- Updated logger and policies imports to reflect new module locations.
- Adjusted values.yaml for branch and log level settings.
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.
