Invia Travel Germany GmbH engaged our services to modernise their search and analytics capabilities using Amazon OpenSearch Service. We designed and implemented a scalable, cost-efficient, and fully managed solution that enabled real-time insights, automated index lifecycle management, and long-term log retention, optimising both performance and operational overhead across their data platform.

The Challenge

Invia Travel faced operational inefficiencies and scalability challenges with their legacy Elasticsearch setup.

• Scalability & Maintenance Issues: Self-managed Elasticsearch struggled with growing data volumes and became complex to maintain.
• High Costs & Limited Visibility: Rising storage expenses and lack of real-time operational metrics hindered efficiency.
• Need for Modernisation: Required a fully managed AWS solution for real-time insights, automation, and long-term retention.

Our Solution

A robust, cost-efficient OpenSearch solution was designed to deliver real-time analytics, long-term storage optimisation, and operational automation at scale for Invia Travel.

• Tiered OpenSearch Architecture: Data ingested via Amazon Kinesis and Amazon Data Firehose; hot nodes for recent data, UltraWarm and cold Amazon S3 storage for older logs using Index State Management (ISM) policies.
• Automated Data Lifecycle: ISM handles rollover, replica reduction, merging, snapshotting, and deletion, minimising manual overhead.
• Performance & Cost Optimisation: AWS Graviton3 nodes, adaptive shard sizing, tuned refresh intervals, and compression cut query latency by 40% and reduced storage costs.
• Integrated Observability & Resilience: Amazon CloudWatch, OpenSearch Dashboards, and automated alerts support monitoring; architecture spans multiple AZs for high availability.

The Result

Invia Travel’s OpenSearch implementation delivered measurable improvements in performance, cost efficiency, automation, and observability.

• Significant Cost & Performance Gains: Achieved a 90% reduction in long-term storage costs and 45% faster query performance on hot data.
• Enhanced Indexing & Automation: 30% boost in peak indexing throughput and full automation of lifecycle operations via ISM.
• Improved Monitoring & Response: Centralised observability with dashboards, CloudWatch alerts, and automated snapshots for rapid incident response.
• Stronger Compliance & Resilience: Enabled cross-region snapshots, enforceable retention policies, and routine disaster recovery testing.

The post Invia Travel: Scalable Search and Analytics with Amazon OpenSearch Service appeared first on Rambunct Consulting.

The Challenge

• Inconsistent development environments caused onboarding delays and integration issues across member organisations
• Collaboration barriers from tooling conflicts, firewall restrictions, and lack of centralised access
• Complex, fragmented workflows for generating and sharing OpenTravel model artifacts
• Limited scalability and high costs in maintaining local dev setups, especially during peak cycles or contractor onboarding

Our Solution

The solution uses Amazon WorkSpaces to provide secure, pre-configured virtual desktops for OpenTravel Java development. It enables fast onboarding, consistent tooling, and integrated DevOps workflows to streamline collaboration and artifact delivery.

• Amazon WorkSpaces are pre-configured for OpenTravel Java development, enabling quick onboarding, secure access, and streamlined API artifact generation
• Standardised development environments using Amazon WorkSpaces with pre-configured Java stacks, Eclipse IDE, and OpenTravel toolkits
• Automated provisioning and secure access for rapid onboarding and role-based collaboration across travel industry stakeholders
• Integrated DevOps workflows with source control, build/test pipelines, and artifact generation for XSD and Swagger
• Compliance and control through workspace isolation, activity logging, and secure access to shared repositories and specs

The Result

The AWS-based solution significantly improved how OpenTravel member organisations develop and collaborate on travel industry standards. By standardising environments, automating workflows, and centralising access, the platform boosted productivity, reduced costs, and ensured compliance, empowering faster and more consistent contributions across the ecosystem.

• Faster onboarding and development with 90% reduced setup time and 60% higher productivity through automation and optimised tooling
• Improved consistency and collaboration with standardised environments and centralised access for all stakeholders
• Cost and infrastructure efficiency achieved with 50% lower infrastructure costs and 99.8% platform availability
• Governance and compliance supported through full audit trails, secure role-based access, and alignment with OpenTravel standards

The post OpenTravel: Amazon WorkSpaces for DEx Development Platform Case Study appeared first on Rambunct Consulting.

RentLord launched a global rental marketplace using AWS Elastic Beanstalk, Amazon CloudFront, and Amazon Aurora PostgreSQL, delivering a scalable, secure, and high-performance booking platform that enables seamless experiences for travellers and property owners worldwide.

The Challenge

• Complex deployment requirements and peak booking traffic overwhelmed legacy systems.
• Slow global content delivery reduced booking conversion and search usability.
• High query volume and transactional demands required powerful, scalable databases.
• Payment security and compliance needed robust protection and high availability.

Our Solution

• Elastic Beanstalk enabled rapid Django deployments with zero-downtime updates and auto-scaling.
• Application Load Balancer ensured intelligent traffic routing and high availability.
• CloudFront accelerated property image delivery and real-time booking interface performance.
• Aurora PostgreSQL with read replicas handled intensive search queries and payment transactions.

The Result

The AWS solution enabled RentLord to deliver a fast, scalable, and reliable rental marketplace experience across geographies and user segments.

• 95% improvement in global page load times via CloudFront CDN acceleration.
• 99.9% uptime enabled uninterrupted search, booking, and payment services.
• 70% reduction in infrastructure management effort through Elastic Beanstalk automation.
• 80% improvement in search performance using optimised Aurora PostgreSQL and Elasticsearch.

The post Rambunct Scales RentLord’s Global Booking Platform with AWS Elastic Beanstalk & Amazon CloudFront appeared first on Rambunct Consulting.