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The client, a data analytics provider serving sectors such as food & beverage, hospitality, and financial services, relied on decades-old on-premises hardware and legacy software. Their analytics platform processed point-of-sale (POS) and transaction data to generate consumer/business insights.
The client's legacy platform was tightly coupled to aging hardware, constraining operational scalability and driving excessive maintenance overhead. Complex maintenance and architectural rigidity inflated operating costs and prevented the system from meeting performance expectations. A full month of POS data required more than 48 hours to process.
The platform was more than two decades old and suffered from poor documentation, inconsistent coding practices, tightly embedded business logic, and outdated programming languages.
Extensive legacy customizations had hardwired architectural constraints, significantly limiting the flexibility to redesign or modernize the system.
The platform processed large data volumes stored in outdated formats, with a high incidence of duplication and inconsistent data structures, directly impacting analytics accuracy and performance.
They engaged us to modernize the stack and deliver clear, measurable improvements:
Primary Objectives
To overcome outdated technology constraints, slow data processing, and hardware dependency, we designed a full-scale modernization strategy centered on reverse engineering and cloud-native architecture. The approach involved deconstructing and documenting the legacy codebase, mapping configuration and execution logic, architecting a scalable cloud framework, migrating data through structured ETL pipelines, and deploying the modernized platform using CI/CD automation to ensure business continuity with zero operational disruption.
We conducted a deep technical audit of the legacy codebase using advanced static analysis tools to understand system architecture, control flow, and structural dependencies. This enabled our team to surface critical components, hidden vulnerabilities, and tightly coupled integrations. Logic paths, data flows, and operational sequences were reverse-engineered and fully documented to create a reliable foundation for modernization.
All functional modules were visually modeled using standardized architecture and UML frameworks to clearly define component relationships. Environment-specific configurations were inventoried, and every external dependency — including libraries, APIs, and third-party services — was cataloged. This resulted in a fully traceable system blueprint that enabled risk-free transformation.
We architected a scalable, cloud-native infrastructure leveraging object storage for high-volume data, managed ETL pipelines for transformation workflows, and cloud data warehousing for analytics performance. Identity-driven access controls, end-to-end encryption, and multi-zone redundancy were built into the architecture to guarantee security, compliance, and high availability.
We migrated data from legacy environments with zero-loss guarantees and minimal operational disruption. Custom transformation logic cleaned, normalized, and standardized datasets to ensure integrity. High-throughput ETL orchestration pipelines were implemented to enable efficient and reliable data movement into the modern analytics environment.
We provisioned AWS cloud environments for development, staging, and production workloads. Automated CI/CD pipelines enabled seamless, zero-downtime deployments. Stress and load validations were performed to benchmark performance, while automated observability and logging frameworks delivered real-time operational visibility and rapid incident response.
Processing time is reduced from days to hours, enabling 3X faster insights and data turnaround.
With up to 70% fewer hardware dependencies and 60% reduced operations costs.
The cloud-based architecture allows the platform to handle increasing data volumes without major rework or hardware upgrades.
Automated workflows and cloud-managed services replaced manual maintenance, freeing up resources and reducing human error by 20%.
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Since 1999, SunTec has been a trusted business process and software engineering partner for technology companies worldwide. Our team of over 1,500 full-time employees delivers innovative services every day to help our clients save time and money.
