Azure VNet Diagram for Kafka Pipeline Architecture

Tech architecture infographic titled "Kafka Pipeline Architecture" using archetype DATA PIPELINE (extract → transform → load). Show a clear executive-friendly end-to-end event streaming system with labeled boxes connected by directional arrows. Include these components as clean vector boxes with small generic icons, canonical English names, and one-line English role descriptions: Producer Apps — "Business systems publish event messages"; API Gateway — "Receives external event submissions over HTTPS/REST"; Kafka Producer Service — "Validates and writes records to Kafka topics"; Kafka Cluster — "Distributed event log storing ordered topic partitions"; Topic Partitions — "Parallel streams of append-only event records"; Schema Registry — "Stores event schema versions for compatibility checks"; Stream Processor — "Transforms, filters, and enriches event streams"; Consumer Service — "Reads events and triggers downstream actions"; Data Lake Storage — "Stores raw and historical event data"; Analytics Database — "Serves reporting and business intelligence queries"; Monitoring Dashboard — "Tracks lag, throughput, and broker health"; Generic Cloud Network — "Private network boundary for platform components". Add optional familiar reference boxes for broad executive readability: Browser / Admin Console — "Views pipeline status and metrics"; Cache — "Speeds up repeated metadata lookups"; Queue / Dead Letter Topic — "Holds failed events for later review"; Operational DB — "Stores application state derived from events". Connect all boxes with arrows showing realistic Kafka data flow: Producer Apps → API Gateway labeled "HTTPS event POST"; API Gateway → Kafka Producer Service labeled "REST JSON payload"; Kafka Producer Service → Schema Registry labeled "Schema check"; Kafka Producer Service → Kafka Cluster labeled "Produce event"; Kafka Cluster ↔ Topic Partitions labeled "Append / replicate records"; Kafka Cluster → Stream Processor labeled "Consume batch"; Stream Processor → Kafka Cluster labeled "Publish transformed event"; Kafka Cluster → Consumer Service labeled "Consumer group read"; Consumer Service → Operational DB labeled "UPSERT record"; Kafka Cluster → Data Lake Storage labeled "Raw event sink"; Stream Processor → Analytics Database labeled "Aggregated metrics"; Kafka Cluster → Queue / Dead Letter Topic labeled "Failed event"; Kafka Cluster → Monitoring Dashboard labeled "Lag / throughput metrics"; Browser / Admin Console → Monitoring Dashboard labeled "HTTPS dashboard view"; Cache → Consumer Service labeled "Metadata lookup". Make the flow technically accurate and visually emphasize Kafka as the central event backbone, with partitions inside the cluster, multiple consumers downstream, and monitoring off to the side. Add a numbered legend 1-7 in English: 1. Event source sends business data to the ingestion layer. 2. Producer service validates payload and schema before publish. 3. Kafka brokers persist records to topic partitions and replicate them. 4. Stream processor consumes events and creates enriched or derived streams. 5. Consumer services read topics independently using consumer groups. 6. Data is written to operational storage, analytics systems, and raw archives. 7. Monitoring tracks broker health, consumer lag, and delivery issues. Visual style: retro 1980s computing meets technical blueprint poster, blueprint cyan palette with cyan, dark navy, grid background, glowing vector lines, subtle CRT monitor accents, geometric arrows, clean isometric or flat hybrid tech-diagram composition, executive-summary clarity, lightly futuristic but professional, not a security or compliance claim. Include editorial developer-blog illustration, isometric or flat tech-diagram style, vector-clean infographic layout. All text MUST be written in English (array). Every heading, label, caption, legend and metric name in the image must be in English — not English. Spell each English word correctly using English characters and diacritics. Numbers stay as digits, no real cloud-vendor logos (AWS / GCP / Azure) — use generic cloud icons, no watermarks No real cloud-vendor logos (AWS, GCP, Azure) beyond generic cloud icons. Common protocol names (HTTPS, TCP, JWT, OAuth, REST, GraphQL) stay in canonical English form. No security-claim overstatements (do not present diagrams as audited reference architectures).