feat(docs): add telemetry backend streaming architecture design

docs/telemetry-backend/README.md

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+# Telemetry Backend Service
+
+This directory contains the architectural design, data pipeline specifications, and documentation for the real-time telemetry processing backend. The system is engineered to ingest, process, and visualize application performance metrics, user interactions, and crash logs transmitted from the Android Registration Client.
+
+## System Overview
+
+To safeguard client application performance and ensure reliable offline operations, the telemetry pipeline decouples high-throughput data ingestion from downstream analytical processing:
+
+* **Client Buffering & Upload:** Telemetry events are queued locally on the Android device. Once network connectivity is established, logs are transmitted via the **TUS protocol** to handle large or interrupted file transfers safely.
+* **High-Throughput Ingestion:** The `tusd-server` receives chunked file uploads and saves completed telemetry files to a shared local storage volume.
+* **Zero-Overhead Log Shipping:** A lightweight **Vector** sidecar container monitors the shared volume, parses completed JSON files, and instantly streams them to a distributed message queue.
+* **Stream Processing & Validation:** **Apache Spark Streaming** consumes the raw telemetry stream, executing real-time data cleansing, schema validation, and PII filtering.
+* **Time-Series Storage:** Cleaned telemetry datasets are persisted in an analytical, time-series data warehouse optimized for rapid aggregations.
+* **Real-Time Visualization:** An operational dashboard engine queries the datastore to provide administrators with live monitoring, system health metrics, and crash reporting.
+
+---
+
+## Technical Stack
+
+* **Ingestion Protocol:** [TUS Resumable Upload Protocol](https://tus.io/) for resilient file transfers over HTTPS.
+* **File Receiver:** `mosip/tusd-server` (the official TUS protocol implementation).
+* **Data Pipeline & Shipper:** `timberio/vector` – a high-performance telemetry agent configured to watch, parse, and route JSON logs.
+* **Message Broker:** Apache Kafka (managed via ZooKeeper) for high-throughput, fault-tolerant streaming queues.
+* **Stream Processing Engine:** Apache Spark Streaming (for micro-batch processing, validation, and PII masking).
+* **Data Warehouse:** Analytical, time-series optimized datastore (e.g., ClickHouse or PostgreSQL).
+* **Visualization:** Dashboard engine (e.g., Grafana or Apache Superset) for live operator monitoring.
+
+---
+
+## Architectural Data Flow
+
+The complete end-to-end data pipeline follows a strict reactive and decoupled flow:
+
+```
+[ Android Client ] 
+       │ (TUS Protocol / HTTPS)
+       ▼
+[ TUSD Server ] ──────> [ Shared Volume ] ──────> [ Vector Sidecar ]
+                                                          │ (TCP / JSON)
+                                                          ▼
+                                                   [ Apache Kafka ]
+                                                          │
+                                                          ▼
+                                              [ Apache Spark Streaming ]
+                                              (Validation & PII Filtering)
+                                                          │
+                                                          ▼
+                                              [ Time-Series Database ]
+                                              (ClickHouse / PostgreSQL)
+                                                          │
+                                                          ▼
+                                              [ Operational Dashboard ]
+                                              (Grafana / Apache Superset)
+
+```
+
+---
+
+## Pipeline Stage Specifications
+
+### 1. Ingestion & Edge Shipping (`tusd` ➔ `Vector` ➔ `Kafka`)
+
+* **TUSD Server** accepts the chunked data streams from the Android client and writes them out as completed JSON files.
+* **Vector** monitors the landing directory, reads new files instantly, flattens the structure, appends an ingestion timestamp (`ingest_timestamp`), and forwards the raw event payload directly to the Kafka topic `registration-client-telemetry`.
+
+### 2. Stream Processing (`Kafka` ➔ `Spark Streaming`)
+
+* **Spark Streaming** connects to the Kafka broker via a micro-batch architecture (e.g., 5-second trigger intervals).
+* **Processing Tasks:**
+* **Schema Enforcement:** Parses raw string payloads into strongly typed DataFrames matching the telemetry schema.
+* **Data Cleansing:** Filters out malformed entries and handles missing parameters.
+* **Security & Compliance:** Masks or strips out Personally Identifiable Information (PII) before the data leaves the processing boundary.
+
+
+
+### 3. Storage & Analytics (`Spark` ➔ `Database`)
+
+* Validated events are written concurrently into structured tables partitioned by time (e.g., `event_date`) to optimize historical query speeds.
+* Indexes are placed on critical high-cardinality fields such as `client_id`, `session_id`, and `log_level`.
+
+### 4. Monitoring (`Database` ➔ `Dashboard`)
+
+* The visual dashboard layer maintains direct read-only access to the database.
+* **Core Panels:**
+* **System Health:** Active device counts, network sync latencies, and upload completion success rates.
+* **Application Metrics:** UI interactions, page load speeds, and feature utilization stats.
+* **Stability Matrix:** Real-time crash tracking, unhandled exceptions, and fatal error distribution grouped by client version.

docs/telemetry-backend/architecture-design.md

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+# Architecture Design: Telemetry Streaming Pipeline
+
+This document details the end-to-end data flow and architectural components governing the telemetry backend.

docs/telemetry-backend/data-schemas.md

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+# This document outlines the structured JSON communication schemas established between the Flutter client application and the streaming backend ingest framework.