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TagB: A scalable parking and valet management system

Category: Automobile/SaaS

Services: Managed Engineering Teams, Resilience, Fault Tolerance, Data Recovery Planning and Implementation, Cloud Architecture Design, and Review

  • 20% reduction in infrastructure costs
  • 30% reduction in data access latency
  • 15% improvement in system stability
  • 30% improvement in data isolation and protection


TagB specializes in parking management, offering parking facilities, revenue management, enforcement, and consulting services. The company aimed to create a scalable solution for maintaining a full parking and valet management system for multiple vendors, focusing on multi-tenancy, security, database, and infrastructure as code.


  • TagB encountered challenges maintaining availability and resilience for their web applications.
  • The lack of a robust security system exposed the applications to potential cyber threats and malicious traffic.
  • The absence of real-time visibility into user behavior hindered data-driven decision-making for business growth.
  • Manual infrastructure management resulted in errors and inefficiencies in parking reservations and valet services.
  • Managing parking locations, schedules, and valet services took time due to the need for a reliable infrastructure.
  • TagB customers often experience lag during the reservation process 
  • TagB needed reliable and secure code with data recovery mechanisms and a highly available, cost-effective infrastructure

Proposed Solution & Architecture

  • Our team designed and implemented robust microservices-based architecture for higher system resilience.
  • Our experts configured a CDN frontend service to distribute private Amazon S3 bucket data closer to users, reducing latency and improving availability and resilience against sudden surges.
  • Our team set up Amazon RDS to create read replicas of your database, which can be used for scaling read operations and enhancing resilience.
  • We used Amazon ECS to dynamically adjust capacity and maintain steady performance despite varying workloads or server failures.
  • Created a safe and reliable payment management system that allows users to add several payment methods.
  • Our team implemented rate-based rules in AWS WAF to control the number of requests from individual IP addresses, safeguarding the applications from abusive behavior and potential overloads.
  • We ensured data privacy and security with multi-tenancy support using AWS CloudFormation.
  • Our experts implemented Lambda Edge security headers to add security when displaying content through CloudFront from an S3 bucket.

Metrics for Success

  • Microservices architecture design reduced infrastructure costs by 20% and improved the system’s resilience.
  • The configuration of a CDN frontend service resulted in a 30% reduction in data access latency, improving user experience and responsiveness.
  • Setting up read replicas for Amazon RDS increased database read operations scalability by 25%, improving system performance during peak loads.
  • Leveraging Amazon ECS for dynamic capacity adjustments resulted in a 15% improvement in system stability during varying workloads or server failures, ensuring consistent performance.
  • Using AWS CloudFormation to support multi-tenancy improved data isolation and protection by 30%, ensuring compliance with privacy regulations.

Architecture Diagram


AWS Services

  • Amazon RDS: We used Amazon RDS to store application and user data, including user account information, parking lot information, and license plate number recording. Access to Amazon RDS is restricted to specific IP addresses for security purposes.
  • Amazon ECS: We leverage Amazon ECS with EC2 hosts containerized APIs that front-end applications can use to manage the microservices backend and deliver new modules such as advanced parking booking, payment and refund administration, and parking lot creation.
  • AWS Task Definition: We used AWS Task Definition to define commands to ECS; for example, each task will have specific configurations such as data volumes, memory utilization required, and the number of containers needed. 
  • AWS WAF: Our team managed AWS WAF to protect against cyber threats, implemented rate-based rules, and ensured data privacy with multi-tenancy support for TagB’s security needs.
  • AWS Lambda & Lambda Edge Security Headers: Our team used AWS Lambda to compress user profile pictures and implemented Lambda Edge security headers to add security when displaying content through CloudFront from an S3 bucket.
  • S3 Bucket: We used S3 to store users’ documents, including admins, customers, and clients, were stored in an S3 bucket. 
  • Amazon CloudFront: Our team used Amazon CloudFront to distribute static and dynamic content across application front-ends, creating customized user experiences with high-speed delivery. 
  • AWS SES & SNS: We used AWS SES to automate sending emails to users, such as registration confirmations and bill delivery. AWS SNS sends notifications for offers, refunds, pass expiration, and more.
  • Amazon ECR: Our team leveraged Amazon Elastic Registry(ECR) to store Docker images for deployments.
  • Application Load Balancer: Our team used ALB to distribute traffic to multiple targets like EC2 instances or ECS containers in different availability zones for multiple requests from users worldwide. 

Metrics for Success

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