Enterprise Case Study// MACHINEWISE
MachineWise
Industrial telemetry platform cutting time-series DB storage by 70%, architecting zero-loss disaster recovery, and streaming live factory dashboards.
ReactViteNode.jsExpressMongoDBRedisDocker ComposeMQTT
-70%
DB Storage
0
DR Data Loss
99.9%+
Platform Uptime
The Problem & Operational Context
High-frequency CNC machine telemetry was bloating time-series database storage costs, while uncoordinated single-node architectures risked irreversible data loss during factory power and cluster failures.
Key Architectural Decisions
Re-architected the persistence model to an event-driven on-change compression and tiered aggregation strategy, paired with automated daily archive backup pipelines across MongoDB replica sets.
Interactive Architecture Topology
Generic Industrial Telemetry Architecture
Core Pipeline ERP Webhook Archive / DR
Java Microservices & Redis
(Event Processing & Rollups)Key SLA:-70% Database Storage Bloat
Filters static steady-state sensor values; compresses continuous time-series on change; maintains rolling window aggregations in Redis.
Tech Stack:Java 17 • Spring Boot • Redis Pub/Sub•Role:On-change delta compression and real-time buffering
Key Architecture & Capabilities
- Slashed database storage consumption by over 70% while accelerating query execution for historical production reports.
- Engineered automated daily archive-based backup and restoration pipelines across MongoDB replica sets, recovering corrupted clusters with 0 data loss.
- Built responsive real-time web dashboards using React, Vite, and WebSockets for live CNC machine telemetry and OEE tracking.
Proprietary Industrial IP • Architecture patterns shared with permission
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