• Accurate Data Capture & Tracking
  Ensuring the app reliably records detailed bowling metrics like delivery type, count, and intensity without manual errors poses a challenge, especially in a real-time sports environment.

• Scalability & Performance
  As user adoption grows, maintaining app performance and scalability will be critical, particularly during peak usage times. Designing a backend that can handle large volumes of data efficiently is essential.

• User Engagement & Retention
  Encouraging consistent use of the app among bowlers can be challenging. Building features that foster community interaction, personalized plans, and gamified challenges will be crucial to retaining users.

• Cross-Platform Compatibility
  Delivering a seamless user experience across both iOS and Android devices requires rigorous testing to address device-specific issues, screen resolutions, and performance variations.

• Challenge 1: High Computational Demand: The WRF model’s capacity to produce highly detailed meteorological datasets necessitates extensive computational power, which made running it on the customer’s existing local infrastructure impractical. The challenge was to find a solution that could efficiently handle large-scale data generation with optimum costing.
  • Solution: This challenge was met by implementing an AWS-based high-performance computing (HPC) cluster, specifically AWS ParallelCluster, which provided the necessary computational resources to run the WRF model efficiently. The jobs on ParallelCluster were created and managed dynamically using AWS Stepfunction and AWS Lambda by utilizing Slurm APIs
• Challenge 2: User Experience and Commercialization: To monetize their meteorological data, the customer needed to create an accessible, user-friendly portal where external users could easily select regions, adjust data resolution, and purchase datasets. The portal needed to be intuitive, efficient, and fully capable of handling secure transactions, which was essential for the success of the customer’s business model.
  • Solution: The customer addressed this challenge by developing a web-based portal using AWS Amplify, integrated with AWS WAF and Shield for security, and managed via Amazon API Gateway. This platform provided a seamless user experience, enabling external customers to effortlessly interact with the system, select their data parameters, and complete purchases, thereby facilitating the commercialization of their datasets and enhancing revenue streams.

• Reuse the existing code logic  
• Map the complicated calculation logic from the existing app to the new app 
• Retain all features yet give it a better User experience  

• Diverse data sources- Data Analytics and cleaning up and integration patterns to pull data from different data sources 

• On-premise data connection to data lake migration- Site-to-site Secure AWS connection was implemented  

• Templatized format for creating pipelines- Created scripts of specific format, Deployment scripts, and CI CD scripts  

• Integrated Patient Profile with NDHM: This application seamlessly integrates with NDHM, enabling the swift creation of ABHA IDs and facilitating the exchange of patient health data. By interfacing with the National Digital Health Mission, the system ensures that patient data is standardized, up-to-date, and easily accessible, fostering more informed medical decisions.
• Multi-tenancy Architecture: The system’s ability to cater to multiple hospitals or health providers under a single unified platform is a significant advantage. Each hospital can manage its operations while benefiting from centralized updates and features, ensuring scalability and simplifying administrative tasks.
• Data Encryption at Rest and In Transit: Implemented encryption using AWS Key Management Service (KMS) for both data at rest (S3, EBS, RDS) and in transit (SSL/TLS) to ensure compliance with GDPR and HIPAA requirements for securing sensitive data.
• Identity and Access Management (IAM): Designed and enforced strict least-privilege access policies using AWS IAM. This included creating custom roles and policies with granular permissions for specific users and services, ensuring only authorized personnel had access to sensitive data.
• AWS Config and Compliance Rules: Set up AWS Config to track and audit configuration changes across the environment. Applied AWS Config Rules to continuously monitor compliance against GDPR and HIPAA requirements, such as encryption enabled on S3 buckets and logging for API Gateway and Lambda.
• Audit Logging and Monitoring: Configured AWS CloudTrail and Amazon CloudWatch for continuous logging and monitoring of API calls, changes, and actions within the AWS environment. This was crucial for meeting HIPAA requirements for audit trails and GDPR’s data access visibility.
• VPC Flow Logs and Security Groups: Deployed Virtual Private Cloud (VPC) with properly configured flow logs to monitor and log network traffic. Used AWS Security Groups and Network ACLs to ensure secure network segmentation and prevent unauthorized access to sensitive resources.
• Data Residency and Data Transfer Controls: Implemented controls to ensure data residency compliance by restricting data storage and processing to specific AWS regions as required by GDPR. Utilized VPC endpoints and AWS Direct Connect to secure data transfers and reduce the exposure to the public internet.
• Backup and Disaster Recovery: Designed an automated backup strategy using AWS Backup to meet GDPR’s requirement for data recoverability, ensuring regular snapshots of critical databases (e.g., RDS, DynamoDB) and storing them in encrypted S3 buckets across different regions for redundancy.

We encountered some ssues as below: 

• Establishing a pre-defined system criterion which needs to be met before uploading the picture. This includes deciding threshold value for the image processing algorithms to be implemented for removal of white spaces present on the side of ID card and skewness present in the image captured
• The Canny algorithm used for border detection had some limitations and didn’t work in all the scenarios where ID card had more prominent rectangle drawn on it or an image with white background of a white ID card. These cases didn’t gave a correct collection of lines and may have resulted in incorrect border detection. To overcome this problem we implemented FindContours on binary image which gave us a more reliable result
• We had to cater for devices of varying power and capability since this solution implementation had to run on various platforms. As we got into the testing phase we had to avoid large variation in responsiveness depending on the device used

We encountered some issues as below: 

• We faced some issues pertaining to system changes not working as per the new standards
• Creating a backup strategy without having proper  backup tools in place
• Ensuring this end-to-end migration did not lead to data loss
• Additional security enhancements are to be added to ensure unauthorized users do not modify reports. 

We encountered some issues as below:

• AWS setup and pricing were complicated to understand as it is based on usage and consumption, which was a difficult thing to assess at the start of the application
• Ensuring data privacy and security is of utmost importance in this case. Since devices can be hacked without much effort due to poor encryption and that could allow unauthorized access
• Impeccable quality assurance of the whole setup was to be achieved in this case of the pharmaceutical industry, which involves dealing with medicines are surgical instruments, so there was a need for honest sharing of information if anything was not going as expected.
• Understanding the client’s vision of how they needed the UI was challenging.

• It was a bit of a challenge to ensure the new environment after migration meets all of the compliance criteria and still remain cost effective.
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