Update requirements and simplify CI workflow by integrating quality gate from template. Bump sientia-dataops-library version to 1.4.7 and streamline quality-gate.yml to use a shared workflow configuration.
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.
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 requirements for sientia-dataops-library to version 1.3.7, add ServiceMonitor configuration for Prometheus in values.yaml, and refactor pod_id usage in Gates and Redis activities for improved consistency in metric tracking.
Update sientia-dataops-library dependency version from 1.3.0 to 1.3.3 in requirements.txt and change GITHUB_BRANCH in values.yaml to SIENTIAPDE-1163-alterar-dinamica-de-notificacoes-para-usar-o-mongodb-ao-inves-do-kafka for improved notification handling.
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.
