99.99%
System High Availability10x
Time handling more client100%
Client application provisioningOne of our US-based clients has been providing technology services to large organizations since its founding in 2007. Over the years, it has grown into a major player in enterprise services. Its SaaS platform processes large volumes of data daily and serves a geographically dispersed client base. Despite its success, the platform relied on legacy components that were not high-availability aware, creating a risk of system failures. To maintain its market-leading position, the client engaged TenUp to deliver a robust, on-premises solution that would make their SaaS application highly available. This engagement focused on strengthening the client’s high availability architecture, initiating a structured application modernization effort, and optimizing their on-premises infrastructure.
The client needed to ensure their SaaS platform could deliver uninterrupted service to users and support real-time operations for new client provisioning. Key requirements included highly available role-based authentication, reliable backend services to prevent system-wide failures, mechanisms to avoid “split-brain” scenarios that could corrupt data, and continuous health monitoring of all node instances to preempt potential issues. This required a strong focus on failover and disaster recovery strategies as well as effective cluster management. Meeting these objectives was critical to maintaining uptime, protecting data integrity, and supporting 24/7 operations.
Ensuring a cost-effective, seamless, and resilient high-availability solution that can integrate with the existing system, avoid downtime, prevent data issues, and include continuous health monitoring. To modernize the SaaS platform, while meeting our client’s unique requirements, the TenUp team faced the following challenges:
Our application modernization efforts to deliver a highly available and resilient platform included implementing a robust architecture, combining active-passive node design, high availability architecture, high-availability clustering, and failover and disaster recovery mechanisms. The solution focused on the following key areas:
The implemented solution delivered significant improvements in system resilience, cost efficiency, and long-term maintainability:
We completed this project ahead of the deadline, successfully updating our client’s flagship SaaS platform to meet the evolving needs of a global customer base. The system is now more robust, highly available, and cost-effective than ever, and it has positioned the company to continue delivering a market-leading service.
An effective high-availability architecture includes redundancy (multiple instances of critical components), automated failover mechanisms, load balancing across servers, continuous health monitoring, and disaster recovery planning. These elements work together to minimize downtime and ensure seamless service continuity.
Active-passive architecture ensures that one node remains active while the backup continuously monitors system health. In case of failure, the standby node automatically takes over, providing seamless failover. This setup prevents service disruption, maintaining high availability with minimal complexity.
Effective disaster recovery strategies include real-time data replication, geographically distributed data centers, automated failover processes, and regular testing of recovery protocols. These strategies ensure rapid recovery and minimal data loss during catastrophic events.
Best practices include gradually implementing redundancy, using reliable clustering and load balancing tools, ensuring compatibility with legacy components, automating failover and recovery procedures, and conducting regular testing to validate resilience. Compatibility and seamless integration are critical.
Cloud-native platforms like AWS, Azure, and GCP offer on-demand resource scaling, geographical redundancy, built-in load balancers, and automated health checks. These features simplify the design of resilient, highly available enterprise applications.
Load balancers distribute incoming traffic evenly across multiple servers, preventing overload on individual nodes and enabling health-aware routing. This ensures system responsiveness, reduces downtime, and balances resource utilization effectively.
Preventing split-brain scenarios involves implementing quorum witnesses, consistent heartbeat mechanisms, and proper cluster configuration with reliable leader election protocols. These measures ensure data integrity and prevent data corruption caused by multiple active nodes.
Common pitfalls include underestimating the complexity of replication, neglecting comprehensive testing, relying solely on hardware redundancy without proper failover automation, ignoring network vulnerabilities, and not planning for scaling. These can lead to unexpected outages.