Revolutionizing Import Data Management in Logistics

Solution

Business analysis:

Our group started the project by thoroughly analysing the data management requirements of the logistics sector. To establish the system's fundamental features and data architecture, we worked closely with specialists in shipping, customs, and data science.

Software development approach and testing:

Our microservices architecture was chosen in order to guarantee flexibility and scalability. Three-week sprints of the Agile technique were used in the development process to enable frequent input and iterative changes.

Key features implemented:

• User and Customer Management with Role-Based Access Control (RBAC)
• Comprehensive Product and Catalog Management
• Real-time Inventory Tracking
• Efficient Booking and Delivery Management
• Vehicle and Car Management systems
• Integrated Calendar and Event Management
• Customizable Settings for user preferences and system configurations

To handle the massive volume of data, we implemented a hybrid data storage solution using PostgreSQL for transactional data and Elasticsearch for fast, complex queries on millions of import records.

DevOps Implementation:

• CI/CD Pipeline:

  • Using Jenkins and GitLab, we built a strong CI/CD pipeline that made automated testing and deployment procedures possible.

• Containerisation and Orchestration:

  • Docker was used to containerise the application, while Kubernetes on AWS EKS was used for orchestration. This allowed for effective microservices scaling and administration.

• Infrastructure as Code:

  • Consistent and version-controlled infrastructure was ensured through the use of Terraform for managing AWS resources.

• Data management:

  • To efficiently handle and store millions of information, we built an intake pipeline with Apache Kafka and Airflow.

• Search Optimisation:

  • To manage intricate searches on extensive datasets, Elasticsearch was implemented, offering nearly instantaneous search capabilities over millions of import records.

• Monitoring and Logging:

  • Prometheus with Grafana was utilised for real-time monitoring and alerting, and the ELK stack (Elasticsearch, Logstash, Kibana) was put up for centralised logging.

• Security:

  • To defend against online threats, Amazon WAF was put into place. Regular security audits and penetration tests were carried out, and AWS KMS was used to encrypt all sensitive data both in transit and at rest.

• Database management:

  • To accommodate large read loads, read replicas were employed in conjunction with Amazon RDS for PostgreSQL. Database sharding was used to effectively handle the increasing amount of data.

• Caching:

  • Redis was utilised for session management and frequently accessed data caching, which greatly sped up response times.

• Content Delivery:

  • Static assets were sent via Amazon CloudFront, lowering latency for users in various geographic areas.

• Auto-scaling:

  • To manage traffic surges and guarantee steady performance, we integrated auto-scaling at the application and database levels.

• Disaster Recovery:

  • Frequent data backups and recovery exercises were conducted as part of a multi-region failover strategy.

Support

We set up a round-the-clock support staff with three escalation levels. The group created a knowledge base and managed incidents using ServiceNow. To continuously develop the platform, user feedback sessions and performance reviews were held on a regular basis.

Result

With the ability to ingest and analyse one million new records per day, the platform currently successfully manages over 500 million import records. It has a 99.99% uptime rate and serves over 10,000 active users in 50 countries.

Key achievements include: :

• 60% reduction in query response time for complex searches
• Ability to scale to handle 100x increase in data volume without significant performance degradation
• 45% reduction in infrastructure costs through efficient resource utilization and auto-scaling
• 99.99% data accuracy maintained even with high volume data ingestion
• Compliance with GDPR and other international data protection regulations

The robust DevOps practices implemented have allowed for rapid feature development and deployment, with an average time-to-market for new features reduced by 50%. The platform's ability to handle large volumes of data while providing granular insights has positioned the customer as a leader in logistics data management, attracting major shipping companies and customs agencies worldwide.