refactored: to utilise the google adk and production grade agent
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# Pre-emptive Source Environment Discovery (As-Is Architecture)
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## Existing Workload Audit
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- **Workload Summary**: Create a high-scale containerized data ingestion pipeline with least-privilege IAM service accounts and automated validation. (Legacy / Pre-existing Environment)
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- **Current Hosting**: On-Premises Data Center / Legacy VM Infrastructure
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- **Ingress Layer**: Self-managed NGINX Reverse Proxy listening on HTTP/HTTPS
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- **Application Runtime**: Monolithic Application Instance (Single Point of Failure)
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- **Database Layer**: Self-hosted PostgreSQL Instance (Unreplicated, Local Disk)
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- **Queue / Messaging**: Local RabbitMQ Queue Instance
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## Current Operational Pain Points & Bottlenecks
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- Single-instance compute leading to downtime during maintenance windows.
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- Manual scaling capabilities unable to handle unexpected traffic spikes.
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- Unencrypted local storage and unmanaged backups creating data loss risks.
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- Elevated operational overhead and hardware lifecycle costs.
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## Source Component Topology
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- `Client` -> `NGINX Proxy` -> `Monolith Application` -> `Local PostgreSQL / RabbitMQ`
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flowchart TD
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Client[External Client] -->|HTTP/HTTPS| NginxProxy[Legacy NGINX Proxy]
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NginxProxy --> MonolithApp[Monolithic Application VM]
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MonolithApp --> LocalDB[(Self-Hosted PostgreSQL)]
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MonolithApp --> LocalQueue[Local RabbitMQ Queue]
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