🚀 Scalable Salesforce Ingestion Engine with Monitoring Dashboard

A high-throughput, fault-tolerant ingestion system built on Apex to process external data reliably at scale, with retry handling, duplicate-safe operations, and a real-time LWC monitoring dashboard.

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💡 Overview

This project simulates a production-grade integration pipeline where external systems push bulk data into Salesforce.

Instead of directly processing incoming requests, the system uses a staging-first architecture to ensure durability, controlled processing, and failure recovery.

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⚠️ Problem

Typical Salesforce integrations break under real-world conditions:

• No retry strategy → silent data loss
• Duplicate records due to missing idempotency
• Async limits (Queueable/Future) → dropped jobs
• No visibility into failures → hard to debug

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✅ Solution

This system introduces a decoupled ingestion pipeline with:

• Durable staging layer
• Controlled asynchronous processing
• Retry + failure handling
• UI-based monitoring and manual recovery

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🏗️ Architecture

External Client
    ↓
Apex REST API (Validation + Ingestion)
    ↓
IntegrationStaging__c (Durable Store)
    ↓
Async Processing Engine (Queueable)
    ↓
Dynamic SObject Upsert (External ID)
    ↓
Retry Engine (Controlled Attempts)
    ↓
Dead Letter State (Final Failures)
    ↓
LWC Dashboard (Monitoring + Retry)

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🔧 Core Capabilities

🔹 Ingestion Layer

• Accepts bulk JSON payloads via REST API
• Validates structure and normalizes input
• Stores raw payload in staging for reliability

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🔹 Processing Engine

• Asynchronous job execution using Queueable Apex
• Groups records by object type for bulk operations
• Dynamically maps fields using Schema methods
• Performs upsert using External ID

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🔹 Retry & Failure Handling

• Automatic retry with attempt tracking
• Controlled retry limits to prevent infinite loops
• Failed records moved to Dead state after max retries

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🔹 Observability

• Error logs stored with payload + stack trace
• Full traceability of failed operations
• UI dashboard for monitoring and manual intervention

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🔹 LWC Dashboard

• View ingestion jobs in real-time
• Filter by status (Pending, Processing, Completed, Failed, Dead)
• Retry failed jobs directly from UI
• Track retry count and processing state

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📦 Sample Request

[
  {
    "externalId": "123",
    "Source": "Account",
    "Name": "Test Account"
  }
]

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⚙️ Tech Stack

• Apex (REST + Queueable)
• Lightning Web Components (LWC)
• Custom Objects (Staging, Logs)
• Dynamic Schema (SObject mapping)

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🎯 Key Design Principles

• Staging-first ingestion → prevents data loss
• Idempotent processing → avoids duplicates
• Bulk-safe operations → handles high volume efficiently
• Decoupled architecture → improves reliability and scalability
• Operational visibility → enables debugging and control

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📈 What This Demonstrates

• Designing resilient backend systems under failure conditions
• Handling async processing limits in Salesforce
• Building integration pipelines beyond CRUD-level development
• Combining backend processing with UI-based monitoring

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🚧 Future Enhancements

• Event-driven processing using Platform Events
• Exponential backoff with jitter for retries
• Dashboard enhancements (error insights, metrics, charts)
• Configurable mapping via Custom Metadata

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👨‍💻 Author

Robin Sharma Software Engineer | Backend & Integration Systems (Salesforce)