Deploying the updated framework into an existing workflow requires a structured approach to prevent disruptions.
SmartDQRsys New integrates robust data quality capabilities directly into its core, including: smartdqrsys new
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smartdqrsys/ ├── backend/ │ ├── app/ │ │ ├── api/ # REST endpoints │ │ ├── core/ # config, security, logging │ │ ├── models/ # SQLAlchemy/Pydantic models │ │ ├── services/ │ │ │ ├── quality/ # DQ rules engine │ │ │ ├── reconcile/ # reconciliation engine │ │ │ ├── alert/ # anomaly detection │ │ │ └── report/ # report generation │ │ ├── workers/ # Spark/Pandas jobs │ │ └── utils/ │ ├── tests/ │ ├── requirements.txt │ └── Dockerfile ├── frontend/ │ ├── src/ │ ├── public/ │ └── package.json ├── infra/ │ ├── docker-compose.yml │ ├── k8s/ │ └── terraform/ ├── docs/ ├── scripts/ └── README.md Deploying the updated framework into an existing workflow
By analyzing historical foot traffic patterns alongside live data, the platform assigns accurate, shifting arrival windows to prevent lobby congestion. 2. AI-Driven Intent Routing AI-Driven Intent Routing If you want to tailor
If you want to tailor this framework to your engineering needs, please let me know: