Agentic AI Atlasby a5c.ai
OverviewWikiGraphFor AgentsEdgesSearchWorkspace
/
GitHubDocsDiscord
iiRecord
Agentic AI Atlas · Logistics / Fleet Management Stack (Go, PostGIS, Redis, Kafka, React, Grafana)
stack-profile:logistics-fleet-managementa5c.ai
Search record views/
Record · tabs

Available views

II.Record viewspp. 1 - 1
overviewjsongraph
II.
StackProfile overview

stack-profile:logistics-fleet-management

Reference · live

Logistics / Fleet Management Stack (Go, PostGIS, Redis, Kafka, React, Grafana) overview

A fleet management and logistics optimization platform built on Go microservices that ingest real-time GPS telemetry, store geospatial position data in PostgreSQL with PostGIS extensions, and cache active vehicle states in Redis for sub-second dashboard updates. Kafka handles event streaming for route deviation alerts and delivery confirmation workflows. A React frontend with map components provides dispatchers with live fleet visualization. Grafana monitors fleet health KPIs like utilization rate and idle time. Targeted at logistics companies, delivery services, and field-service organizations managing 100+ vehicles. The tradeoff is telemetry volume — high-frequency GPS pings generate substantial write load, requiring careful partitioning and retention policies on the geospatial data layer.

StackProfileOutgoing · 21Incoming · 0

Attributes

displayName
Logistics / Fleet Management Stack (Go, PostGIS, Redis, Kafka, React, Grafana)
description
A fleet management and logistics optimization platform built on Go microservices that ingest real-time GPS telemetry, store geospatial position data in PostgreSQL with PostGIS extensions, and cache active vehicle states in Redis for sub-second dashboard updates. Kafka handles event streaming for route deviation alerts and delivery confirmation workflows. A React frontend with map components provides dispatchers with live fleet visualization. Grafana monitors fleet health KPIs like utilization rate and idle time. Targeted at logistics companies, delivery services, and field-service organizations managing 100+ vehicles. The tradeoff is telemetry volume — high-frequency GPS pings generate substantial write load, requiring careful partitioning and retention policies on the geospatial data layer.
composes
  • language:go
  • library:chi
  • library:pgx
  • library:ioredis
  • framework:react
  • tool:grafana
  • language:typescript

Outgoing edges

applies_to2
  • domain:logistics·DomainLogistics
  • domain:transportation·DomainTransportation
composed_of9
  • language:go·LanguageGo
  • library:chi·LibraryChi
  • library:pgx·Librarypgx
  • library:ioredis·Libraryioredis
  • framework:react·FrameworkReact
  • tool:grafana·ToolGrafana
  • language:typescript·LanguageTypeScript
  • tool:docker·ToolDocker
  • tool:prometheus·ToolPrometheus
follows_workflow2
  • workflow:fleet-maintenance-scheduling·WorkflowFleet Maintenance Scheduling
  • workflow:route-optimization-review·WorkflowRoute Optimization Review
requires_skill_area5
  • skill-area:geospatial-data-analysis·SkillAreaGeospatial Data Analysis
  • skill-area:streaming-realtime-processing·SkillAreaStreaming and Real-time Processing
  • skill-area:caching-strategies·SkillAreaCaching
  • skill-area:data-visualization·SkillAreaData Visualization
  • skill-area:backend-api-design·SkillAreaBackend API Design
used_by_role3
  • role:backend-engineer·RoleBackend Engineer
  • role:operations-analyst·RoleOperations Analyst
  • role:devops-engineer·Role

Incoming edges

None.

Related pages

No related wiki pages for this record.

Shortcuts

Open in graph
Browse node kind