Success StoriesData Management & Data Quality · Case Study

How Codincity Designed a Containerized Hybrid Agent Architecture for Data Quality and Observability Platform

Reimagining Enterprise Data Quality at the Edge

Containerized hybrid agent architecture for enterprise data quality

Overview

Industry: Data Management & Data Quality

Geography: Global

Technologies: Docker, Kubernetes, Helm, OpenTelemetry, Prometheus, Grafana, Redis

The Situation

The customer wanted to modernize its customer-hosted agent architecture to better support enterprise deployment requirements across cloud and on-premises environments. The existing pass-through agent model relied on inbound connectivity and a monolithic architecture, limiting scalability, operational flexibility, and deployment options for enterprise customers.

As their customer adoption grew, the organization required a more flexible architecture capable of supporting local data quality and observability scans while maintaining secure integration with the SaaS platform. The target solution needed to operate consistently across AWS, Azure, GCP, Kubernetes environments, traditional virtual machines, and customer-managed infrastructure.

The customer engaged Codincity to design a modular, containerized hybrid agent architecture that could improve scalability, simplify deployment, strengthen security, and support enterprise-grade operational requirements.

What Codincity Did

Assessed the existing pass-through agent architecture and identified opportunities to improve scalability, deployment flexibility, and operational resilience.

Designed a modular hybrid-agent architecture enabling local data quality and observability scans with secure outbound-only communication to the SaaS platform.

Defined independent service modules including Agent Core, Sync Engine, Scan Executor, and Results Forwarder to improve maintainability and scalability.

Developed a containerization and deployment strategy supporting Docker, Kubernetes, Helm-based deployments, and traditional VM environments.

Designed an observability framework using OpenTelemetry, Prometheus, and Grafana to improve operational visibility and monitoring.

Created an 8-week implementation roadmap covering architecture, development, deployment automation, security hardening, multi-cloud validation, and operational handover.

Business Impact

Established a scalable architecture strategy for customer-hosted deployments across cloud and on-premises environments.

Reduced operational complexity through a modular containerized design and standardized deployment approach.

Improved security posture through outbound-only communication and secure deployment mechanisms.

Created a foundation for multi-cloud deployment and operational consistency across enterprise environments.

Defined a roadmap for enhanced observability, automation, and lifecycle management of customer-hosted agents.

Key Benefits

Scalable deployments — Supports customer-hosted agents seamlessly across cloud and on-premises environments.

Lower operational overhead — Modular, containerized design with a standardized deployment approach reduces complexity.

Stronger security — Outbound-only communication and secure deployment mechanisms improve the security posture.

Multi-cloud ready — Consistent operation across AWS, Azure, GCP, Kubernetes, and traditional VMs.

Faster, flexible deployment — Docker, Kubernetes, Helm, and VM support enable deployment in any target environment.

Improved observability — OpenTelemetry, Prometheus, and Grafana deliver greater operational visibility and monitoring.

Future-ready foundation — Clear roadmap for automation, lifecycle management, and enterprise scale.

What It Means Going Forward

The proposed architecture provides the customer with a path toward a more scalable, secure, and operationally efficient customer-hosted deployment model. By combining containerization, observability, automation, and multi-cloud support, the strategy establishes a foundation for future platform growth and enterprise adoption.

Conclusion

Modernizing platform architecture requires more than infrastructure changes. By designing a modular hybrid-agent framework and defining a structured implementation roadmap, Codincity helped the customer establish a strategy for delivering secure, scalable, and enterprise-ready data quality services across diverse customer environments.