About Project
The QDM Binning App (known as Binning App) is built to help organizations classify claims that come from DMS, MES, and SalesForce CRM systems. Based on the classified claims, the QE/users will identify the root cause of the claims so that they can link them to the existing Jira issues for further product and process improvements.
Project Information
- Industry Manufacturing
- Project Type Web Application
- Country Vietnam
- Team Size 10+
- Tech Stack PostgreSQL, .NET Core, ReactJS, AWS
Project Challenge
- Complex System Integration The DMS, MES, and Salesforce CRM are likely built on different architectures, using different data models, protocols, and technologies. Integrating these disparate systems into a unified Binning App requires careful mapping of data formats, structures, and communication protocols.
- Data Accuracy and Reporting: Data Integrity Ensuring that the data being reported is accurate and has not been corrupted or altered during extraction, transformation, or loading is critical. Any inaccuracies can lead to incorrect root cause analysis and flawed process improvements.
- CI/CD PipelinesUtilize CI/CD GitLab to automate the Continuous Integration and Continuous Deployment (CI/CD) pipelines. These pipelines can help manage the integration of multiple systems by automating the deployment process and ensuring that changes are tested before going live.
Project Results
- API Gateway: Use an API Gateway like AWS API Gateway to abstract the underlying systems and provide a uniform interface for communication. This will help in managing different data models, protocols, and access controls.
- Single Sign-On (SSO) can be utilized for unified access management across different platforms.
- Data Encryption: Use encryption (both in transit and at rest) to protect data being transferred between systems. Ensure that data integrity is maintained by using hashing algorithms to detect any alterations during the transfer process.
- Error Handling Mechanisms:Design robust error handling mechanisms to detect, log, and alert on any data inconsistencies or failures during data extraction, transformation, or loading. Implement automated correction scripts for common errors.
- Real-Time Monitoring:Use tools like AWS CloudWatch for real-time monitoring of data pipelines. This will help in detecting and resolving any issues before they impact reporting.
- Data Reconciliation:Periodically reconcile data between the source systems and the App to ensure that the reported data is accurate and up-to-date.
- Pipeline Configuration:Set up CI/CD pipelines using CI/CD GitLab to automate the build, test, and deployment process. This ensures that changes are continuously integrated and deployed with minimal manual intervention.
- Rollback Mechanism:Implement a rollback mechanism in the pipeline that allows for quick restoration of the previous stable state in case a deployment fails or introduces issues.
Technologies we used
Team
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