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The dashboard will cover Sales, Orders, Revenue, Customers, Products, Inventory, Coupons, and Returns, with daily, weekly, and monthly periods. Deliver both a governed Django/DRF aggregation API and a visible responsive Next.js admin dashboard, plus accuracy, aggregation, permission, and performance tests. ## Existing codebase analysis - The frontend is currently a blank Next.js 16/Tailwind v4 shell, so the analytics dashboard becomes the visible root experience and will establish its own focused dashboard components without introducing unrelated navigation destinations. - The Django modular monolith already owns source-of-truth models for orders/order items, users, products, stock, coupon redemptions, and return requests. Analytics should query these existing tables and add no duplicate fact tables or migrations. - Follow existing staff-only API authorization, repository/service/view separation, `/api/v1/admin/` routing, canonical errors, structured logging, and test conventions. - Use Recharts through shadcn chart primitives for accessible visualization; add only the packages/components required for charts. ## Metric contract and definitions - Accept `period=daily|weekly|monthly`; use timezone-aware UTC boundaries and bucket starts. Default to monthly, with bounded range sizes to prevent expensive scans. - Sales: delivered item quantity and trend by bucket. - Orders: non-cancelled order count and trend; also expose status distribution. - Revenue: sum of delivered order totals in BDT and trend; exclude cancelled/refunded orders to avoid overstating realized revenue. - Customers: distinct purchasing customers in range, with newly registered customer count as supporting context. - Products: live product count, published product count, and top products ranked by delivered item quantity/revenue. - Inventory: available units (`on_hand - reserved`), low-stock SKU count, and out-of-stock SKU count as current operational snapshots. - Coupons: redemption count and discount amount in range, plus redemption trend. - Returns: return-request count and refunded amount in range, plus return trend/status distribution. - Return the selected period, current and prior comparison windows, percentage deltas with explicit null behavior when the prior value is zero, trend buckets, generated timestamp, currency, and metric definitions so the UI and tests share one governed contract. ## Backend implementation - Create `backend/apps/analytics` with app config, repository query layer, metric service/orchestration, serializers, staff-only view, URLs, and tests. - Repository queries use database aggregation (`Count`, `Sum`, `TruncDay/Week/Month`, filtered querysets, `Coalesce`) rather than loading rows into Python; keep query count bounded independent of dataset size. - Service validates period/range, derives aligned current/prior windows, fills empty trend buckets with zeroes, computes deltas safely, and assembles the API contract. - Mount `GET /api/v1/admin/analytics/dashboard?period=...` and register the app in Django settings. No database schema changes are expected. - Log request completion/failure with period, duration, actor, and correlation ID, but no customer PII or raw commerce records. Add short-lived private cache headers only if compatible with current API middleware; never expose analytics publicly. ## Frontend implementation - Replace the placeholder root page with a polished commerce operations dashboard using a narrow desktop sidebar, compact mobile header, top period switcher (Daily/Weekly/Monthly), last-updated state, metric summary cards, a primary revenue/orders chart, category metric strip, top-products table, order-status visualization, and inventory/returns/coupon operational panels. - Create a client dashboard component with typed data contract and period interactions. Fetch the analytics endpoint when available; provide a clearly isolated deterministic demo-data fallback for the v0 preview because the Django service is not hosted by the Next.js preview. Show loading, error/fallback, and responsive states. - Use BDT formatting, semantic HTML, keyboard-accessible controls, chart descriptions/tooltips, restrained borders, square-to-medium radii, strong typographic hierarchy, no gradients, and responsive grid/flex layouts. - Update global design tokens and metadata to a refined warm-neutral/emerald enterprise theme while retaining light/dark compatibility. ## Files to create or modify - Backend create: `backend/apps/analytics/{__init__,apps,repositories,services,serializers,views,urls}.py` and `backend/apps/analytics/tests/`. - Backend modify: `backend/config/settings/base.py`, `backend/config/urls.py`, and only relevant documentation sections. - Frontend modify: `app/page.tsx`, `app/layout.tsx`, `app/globals.css`, `package.json`/lockfile as needed. - Frontend create: focused analytics dashboard/data/chart components and shadcn chart primitives under `components/`. ## Security and reliability - Require authenticated staff/admin access; reject customers and anonymous callers. - Allowlist period values and enforce bounded windows; do not accept arbitrary model names, fields, SQL fragments, or unbounded date ranges. - Aggregate customer data only; never return names, emails, phones, addresses, or per-customer records. - Use stable metric definitions and timezone boundaries to prevent double counting; document exclusions and snapshot-versus-period semantics. - Add indexes only if query-plan evidence shows existing indexes are insufficient; otherwise avoid unnecessary migrations. ## Testing and verification - Accuracy tests seed orders across included/excluded statuses and boundaries, products/order items, users, inventory, coupon redemptions, and returns/refunds, then assert exact totals and currency values. - Aggregation tests cover daily/weekly/monthly bucket alignment, zero-filled gaps, prior-period deltas, boundary timestamps, invalid periods, and staff-only permissions. - Performance tests assert bounded query counts for small versus larger datasets and validate the endpoint remains database-aggregated; avoid brittle wall-clock thresholds in shared CI. - Run Django checks, migration drift check, Ruff, mypy, targeted analytics tests, and the full backend suite. 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