Proposal for Integrating Apache Kafka and Redis for Real-Time Dashboard

 Executive Summary

An existing example dashboard stack—comprising a Next.js front-end, a legacy monolithic API that uses SignalR and a timer service, a database, an in-memory cache, and a TTForwarder console application (a hypothetical application that forwards real-time data)—suffers from latency, scaling limits, and fragile caching.
A modernized architecture that pairs Apache Kafka for high-throughput data streaming with Redis for low-latency caching and Pub/Sub notifications can deliver true real-time performance, simpler operations, and easier horizontal scalability.

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Current Challenges

Pain Point	Impact
Bloated monolith	The legacy API is large, tightly coupled, and hard to maintain.
Inefficient ingestion	TTForwarder pushes updates directly to the database; replacing it with a polling .NET service introduces additional latency.
Unreliable cache	In-memory caching is non-persistent—data vanishes on restart.
Update latency	SignalR combined with timer-based polling does not guarantee sub-second updates.

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Proposed Architecture at a Glance

vbnet
Trading Technologies (TTSDK)
          │
          ▼
 ┌────────────────┐
 │ TTDataIngester │  ➜  Kafka topic: tt_raw_data
 └────────────────┘
          │
          ▼
 ┌────────────────┐
 │  DataProcessor │  ➜  Kafka topic: tt_processed_data + DB writes
 └────────────────┘
          │
          ▼
 ┌────────────────┐
 │  CacheUpdater  │  ➜  Redis KV + Redis Pub/Sub (tt_updates)
 └────────────────┘
          │
          ▼
 ┌────────────────┐      WebSocket
 │  DashboardAPI  │ ─────────────────►  Next.js Dashboard
 └────────────────┘

Component	Role	Tech Highlights
TTDataIngester	Subscribes to TTSDK streams and publishes raw data to Kafka.	Confluent Kafka .NET client
DataProcessor	Consumes raw data, performs calculations, writes to DB, and republishes processed messages.	Stateless C# workers
CacheUpdater	Keeps Redis in-sync and issues real-time notifications via Redis Pub/Sub.	StackExchange.Redis
DashboardAPI	Serves initial data from Redis and pushes updates to WebSocket clients.	Minimal-API + WebSockets
Next.js Dashboard	Renders data, maintains a WebSocket connection for live updates.	SSR + React hooks

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Data Flow

1. Ingestion – TTDataIngester → tt_raw_data topic

2. Processing – DataProcessor → tt_processed_data topic + database

3. Caching / Notify – CacheUpdater → Redis key-values + tt_updates Pub/Sub

4. Initial Load – DashboardAPI fetches cached data for the Next.js client

5. Real-Time Push – DashboardAPI listens on tt_updates and forwards updates over WebSockets

6. UI Refresh – The dashboard updates UI elements immediately on receipt

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Key Benefits

• Real-Time Performance – Kafka + Redis Pub/Sub achieve sub-second end-to-end latency.

• Horizontal Scalability – Independent services scale out; Kafka partitions and Redis clustering handle load spikes.

• Reliable Caching – Redis persistence eliminates data loss on restarts.

• Simplified Maintenance – Replacing the monolith with focused services reduces code-base complexity.

• Fault Tolerance – Kafka replication and Redis AOF/RDB snapshots provide durability.

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Implementation Checklist

• Kafka – Create tt_raw_data and tt_processed_data topics with appropriate partitions/replicas.

• Redis – Enable AOF persistence; secure with TLS; expose only to internal services.

• DashboardAPI – Use a lightweight WebSocket implementation (e.g., native System.Net.WebSockets or SignalR in hub-less mode).

• Security – Encrypt traffic (Kafka SASL_SSL, Redis TLS) and restrict network access.

• Monitoring – Track Kafka consumer lag, Redis memory usage, and WebSocket connection counts.

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Potential Challenges & Mitigations

Challenge	Mitigation
End-to-end latency spikes	Deploy Kafka and Redis on low-latency hosts; tune broker and OS network settings.
Data consistency	Make DataProcessor idempotent and use Kafka offsets plus DB constraints to avoid duplicates.
Scaling write load	Add Kafka partitions; spin up more CacheUpdater instances; shard Redis if needed.
Failure recovery	Enable Kafka dead-letter topics and automatic retries; configure Redis replication failover.

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Comparison with the Current Setup

Aspect	Legacy Approach	Proposed Approach
Data ingestion	TTForwarder writes directly to DB (adds latency)	Stream to Kafka for immediate downstream processing
Processing	.NET service updates DB only	DataProcessor writes to DB and re-publishes results
Caching	Non-persistent in-memory cache	Redis persistent cache
Real-time delivery	SignalR + timers (variable delay)	Redis Pub/Sub + WebSockets (instant)
Scalability	Limited by single monolith	Kafka + micro-services scale independently
Reliability	Risk of data loss on restarts	Kafka replication + Redis persistence

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Adopting this Kafka-plus-Redis architecture replaces a fragile, tightly coupled system with a responsive, modular, and future-proof platform that delivers a true real-time trading dashboard experience.