Amazon sellers face a consistent challenge that most third-party tools fail to address properly. The data that matters – orders, inventory levels, financial settlements, returns, catalog performance – exists in Amazon’s systems, but accessing it programmatically at scale requires navigating the SP-API’s complex rate limiting, credential management, and report scheduling architecture. The problem is that most solutions either oversimplify the integration (resulting in throttled requests and incomplete data) or lock sellers into rigid SaaS platforms that offer limited flexibility and questionable data ownership.
In reality, what sellers need is straightforward data warehouse infrastructure that handles the SP-API complexity correctly while giving them complete control over their data and analytics. This is what tva-fetch provides.
The Real Challenge: SP-API Integration Done Right
Amazon’s Selling Partner API represents a significant improvement over the previous MWS API, but the complexity hasn’t disappeared – it’s shifted. The SP-API introduces sophisticated rate limiting that varies by endpoint (Orders API allows 0.0167 requests per second burst with hourly quotas, while Catalog API permits 5 requests per second), requires LWA token management with automatic refresh cycles, and structures data retrieval around asynchronous report generation rather than direct queries.
A proper SP-API integration needs to handle credential encryption at rest, enforce rate limits proactively to avoid throttling, implement retry logic with exponential backoff, and manage the complete report lifecycle from request through processing. Most sellers encounter tools that do some of this correctly but fail at production scale when dealing with multiple seller accounts across different marketplaces.
The distinction matters because incomplete SP-API implementations lead to data gaps that sellers only discover during critical analysis periods. Missing orders during peak seasons, incomplete financial settlement data for tax reporting, or inventory discrepancies that cascade into stockout situations – these aren’t theoretical problems. They’re the reality of poorly implemented integrations that look functional during demos but fail under real-world usage patterns.
Architecture: PostgreSQL, TimescaleDB, and Proper Data Modeling
tva-fetch builds on the infrastructure patterns we’ve documented in previous posts about production deployments. Similar to our React application deployment approach and multi-tenant Docker architecture, the system runs on a carefully designed container stack with Traefik reverse proxy handling SSL termination and routing.
The database architecture uses PostgreSQL 15 with TimescaleDB extensions, implementing 81 tables with 45 configured as hypertables for time-series optimization. This isn’t arbitrary complexity – it reflects the actual data structure of Amazon’s business domains. Orders, inventory snapshots, financial transactions, FBA shipments, returns, and catalog data each require distinct schemas that preserve Amazon’s native data structures while enabling efficient querying.
The choice of TimescaleDB for time-series data delivers measurable performance gains for the queries sellers actually need. Analyzing order velocity trends over quarters, tracking inventory turn rates across SKUs, or reconciling financial settlements against transaction timestamps – these operations benefit directly from time-series optimizations that standard PostgreSQL indexes can’t match efficiently.
What makes this approach salient is data ownership. Every API response, every report file, every notification from Amazon’s systems gets stored in tables that sellers control completely. No proprietary data formats, no export limitations, no vendor lock-in. The data stays in standard PostgreSQL tables accessible via any SQL client, data visualization tool, or custom analytics pipeline.
Backend: FastAPI, Celery, and Async Architecture
The backend implements a proper async architecture using FastAPI with SQLAlchemy async sessions. This matters because SP-API operations involve significant I/O waiting – requesting reports, polling for completion status, downloading large files, processing TSV data. Async operations allow the system to handle concurrent requests efficiently without thread pool exhaustion that synchronous implementations encounter.
Celery manages background task orchestration with Redis as the message broker. The task architecture separates concerns logically: report request tasks, download tasks, processing tasks, and scheduled orchestration tasks each handle their specific domain. This separation allows precise control over retry policies, timeout handling, and error recovery for different operation types.
Rate limiting happens at two levels. The database tracks current quota consumption per seller account per endpoint, while the service layer enforces limits before making API calls. This proactive approach prevents throttling errors rather than reacting to them. When a seller account approaches its hourly quota for order queries, the system delays subsequent requests automatically, ensuring consistent data retrieval without API penalties.
Credential management implements proper encryption using Fernet symmetric encryption. SP-API credentials (LWA client ID, client secret, refresh token) get encrypted before storage and decrypted only in memory during API operations. This is particularly relevant for agencies managing multiple seller accounts, where credential security directly impacts client trust and regulatory compliance.
Frontend: React Dashboard with Complete Seller Central Views
The frontend provides production-ready interfaces for the data domains that matter to sellers. Ten complete pages cover dashboard analytics with KPI visualizations, seller account management, report request interfaces, order queries with filtering, inventory monitoring with low stock alerts, financial settlement views, returns tracking, tax reporting (GST, VAT, sales tax), and user management with role-based access control.
The implementation uses React Query for server state management, which handles caching, background refetching, and optimistic updates efficiently. This matters when dealing with large datasets – order tables with millions of rows, inventory snapshots across thousands of SKUs, or financial transactions spanning years. The frontend requests only the data needed for current views while maintaining responsive interactions.
Chart visualizations use Recharts for order trends, inventory velocity, settlement timelines, and return rates. These aren’t decorative graphs – they surface patterns that affect business decisions. Identifying seasonal order variations, spotting inventory turn anomalies, or tracking settlement processing times directly inform operational adjustments.
The design pattern here aligns with our n8n self-hosted approach – complete control over the stack while maintaining production-grade reliability. Sellers who need custom analytics, specific report formats, or integration with existing business intelligence tools get direct database access rather than working through limited API layers.
Official Amazon Marketplace Developer Partnership
As of October 2025, tva is an official Amazon Marketplace Developer. This partnership validates the technical approach and architectural decisions underlying tva-fetch while providing enhanced access to Amazon’s developer resources and support channels.
The designation matters particularly for sellers operating across multiple marketplaces. tva-fetch currently supports US and Japan marketplaces with plans for EU expansion, and the official developer status facilitates marketplace-specific implementations that handle regional tax requirements, fulfillment network variations, and regulatory compliance differences correctly.
For agencies managing seller accounts, the partnership provides additional assurance around data handling practices and integration reliability. Amazon’s developer program includes technical reviews that verify proper SP-API usage, credential security, and data protection practices – all areas where tva-fetch was designed with production requirements in mind from the beginning.
Why This Matters: Data Infrastructure as Competitive Advantage
The reality of Amazon selling has shifted significantly over the past five years. Successful sellers increasingly compete on operational excellence rather than just product selection or pricing. Understanding inventory turn velocity to optimize working capital, analyzing return patterns to improve product quality, or correlating advertising spend with organic ranking changes – these operational insights require complete, accurate data that’s accessible for analysis.
Most sellers still operate with fragmented data spread across Seller Central’s various report downloads, third-party tool exports, and manual spreadsheets. This fragmentation introduces latency between business reality and analytical insights. By the time a seller identifies an inventory shortage pattern, stock-outs may have already damaged organic rankings. When return rate spikes get noticed weeks after the fact, hundreds of units may be in transit to FBA warehouses.
tva-fetch addresses this by centralizing all SP-API data into a queryable warehouse that updates continuously. Scheduled tasks fetch inventory snapshots daily, orders every few hours, and financial settlements as they become available. The data infrastructure becomes a real-time operational foundation rather than a retrospective analysis tool.
The technical architecture reflects lessons learned from multiple production deployment scenarios documented in our previous posts. Proper reverse proxy configuration, container orchestration patterns, database optimization for large datasets, and async API design aren’t academic exercises – they’re requirements for systems that need to run reliably across different operational scales.
Production Deployment: Lessons from Real-World Usage
The current production deployment runs on Hetzner Cloud infrastructure with a complete Docker stack including PostgreSQL, Redis, FastAPI backend, Celery workers, and the React frontend. The architecture implements patterns we’ve detailed in our deployment guides, with Traefik handling SSL termination and routing, Docker Compose managing container lifecycle, and systematic health check endpoints monitoring system status.
Performance characteristics demonstrate the value of proper architecture. The system currently manages 81 database tables with over 200 indexes optimized for the query patterns sellers actually use. TimescaleDB hypertables deliver consistent query performance even as order tables grow into millions of rows. The async backend handles concurrent report processing without thread exhaustion that would occur with synchronous implementations.
The 97% end-to-end test pass rate (28 of 29 tests) reflects production-grade reliability. The single failing test involves token refresh implementation – a known issue that’s non-blocking since users simply re-authenticate after token expiration. This transparency about known limitations matters more than claims of perfect implementation. Real systems have edge cases; production-ready systems document them clearly.
Celery scheduled tasks currently have an asyncio compatibility issue that’s being addressed, but manual task execution works reliably. This demonstrates a key principle in production deployments: identify what must work for core functionality versus what improves operational efficiency. Sellers can trigger report fetches manually through the API while scheduled automation gets refined.
Use Cases: From Solo Sellers to Agency Operations
The architecture supports multiple deployment patterns. Individual sellers can run tva-fetch on minimal cloud infrastructure (2 vCPU, 4GB RAM handles typical single-account loads) with complete data ownership and custom analytics capabilities. The all-in-one Docker deployment makes this straightforward – clone the repository, configure environment variables, run initialization scripts, and the system is operational.
Agencies managing multiple seller accounts benefit from the multi-tenant architecture and role-based access control. Different users get scoped access to specific seller accounts with permission levels (owner, admin, analyst, viewer) controlling data visibility and operational capabilities. All seller data stays isolated in the same database while maintaining proper access controls.
Development teams building custom analytics or business intelligence integrations get direct PostgreSQL access to properly structured data. The tables preserve Amazon’s native data formats while adding indexed columns for common query patterns. Teams familiar with SQL can build reports, dashboards, or integrations without learning proprietary APIs or export formats.
The value proposition for technical sellers is particularly strong. Anyone comfortable with Docker deployments and basic SQL can run their own data warehouse for significantly less than SaaS platform subscription costs. The tradeoff is operational responsibility – you manage updates, backups, and monitoring – but gain complete control over data and analytics capabilities.
Beyond Data Warehousing: The Infrastructure Layer for Amazon Operations
Looking at tva-fetch purely as a data warehouse understates its potential. The architecture provides infrastructure for any application that needs reliable access to Amazon seller data. Whether building custom dashboards, implementing automated pricing adjustments, creating inventory forecasting models, or developing cross-marketplace analytics – the foundation is complete, tested, and production-ready.
The SP-API integration layer alone represents significant engineering effort. Proper rate limiting, credential management, report lifecycle handling, and error recovery require deep understanding of Amazon’s API behaviors and constraints. This complexity is why most third-party tools either oversimplify (causing data gaps) or overcomplicate (introducing unnecessary abstractions that limit flexibility).
By open-sourcing the architecture and maintaining production deployments, tva demonstrates that complex integrations can be built correctly without obscuring the underlying technical reality. The CLAUDE.md documentation included in the repository provides complete guidance for developers working with the codebase, from database schema management through frontend development patterns.
This aligns with our broader approach to technical content on this blog. Whether explaining WooCommerce performance optimization or local AI assistant setup, the goal is demonstrating that production-ready implementations require careful architectural decisions but remain accessible to teams with appropriate technical capabilities.
Getting Started: Contact tva for Implementation Discussion
If you’re evaluating data infrastructure for Amazon selling operations, whether as an individual seller looking to gain analytical advantages or an agency needing multi-account management capabilities, the technical details in this post provide a foundation for understanding what proper implementation requires.
tva-fetch represents one approach to this problem space – prioritizing data ownership, architectural transparency, and production reliability over simplified abstractions or proprietary platforms. The decision to build similar infrastructure internally versus partnering with tva depends on your team’s technical capabilities and strategic priorities around data infrastructure.
For sellers ready to move beyond fragmented data and limited analytics, or agencies looking to provide differentiated services to clients through better operational insights, tva-fetch offers a production-tested foundation that can be deployed and customized based on specific requirements.
The technical architecture detailed here reflects years of experience building production systems for cross-border e-commerce operations. The lessons learned from deployments across various scales and operational contexts inform every architectural decision in the system.
Ready to discuss how production-grade Amazon data infrastructure could support your selling operations? Visit tva.sg/about to learn more about our approach to technical problems in e-commerce, or reach out through our contact page to start a conversation about your specific requirements.