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Smart Mobility Engineering 100 Production– Grade Labs
Free Download Smart Mobility Engineering 100 Production– Grade Labs
Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With Smart Mobility Engineering 100 Production– Grade Labs, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 11h 46m | Size: 1.04 GB
From siloed mobility data to production-grade smart city platforms with Kafka, MQTT, PostGIS, Kubernetes, Terraform & AI
What you'll learn
Build a complete local smart-mobility development environment using Docker, PostgreSQL/PostGIS, MQTT, Python, and Git.
Engineer spatial data pipelines using PostGIS, pgRouting, GeoServer, GeoJSON, OpenStreetMap data, geofencing, and browser-based mapping.
Architect real-time vehicle telemetry systems with Apache Kafka, MQTT, Kafka Streams, schema evolution, consumer groups, and TimescaleDB.
Deploy edge-computing workloads using Docker, K3s, Helm, MQTT security, device-state management, and edge-to-cloud synchronization.
Integrate GTFS and GTFS-Realtime data to build production-oriented public-transit monitoring, reliability, and alerting systems.
Optimize fleet operations using routing algorithms, clustering, vehicle telemetry, maintenance intelligence, heatmaps, and Google OR-Tools.
Secure mobility platforms with Keycloak, OAuth2/OIDC, JWT, RBAC, Kubernetes network policies, Vault, vulnerability scanning, audit logging, and GDPR-oriented an
Engineer observable and resilient systems using Prometheus, Grafana, Loki, OpenTelemetry, Alertmanager, circuit breakers, autoscaling, health checks, and chaos
Automate cloud-native infrastructure with Terraform, Kubernetes, GitHub CI, ArgoCD, ingress, certificates, blue-green deployments, database migrations, and disa
Architect and validate a PhD-level capstone smart-city platform capable of handling 10,000 concurrent vehicle streams, sub-200 ms target processing latency, sec
Requirements
Required Knowledge
1. Basic Linux command-line usage
2. Basic Python programming
3. Basic SQL
4. Basic Git concepts
5. Basic understanding of containers is helpful but not required
Recommended Software
1. Linux — Ubuntu 22.04/24.04 LTS recommended
2. Python 3.12+
3. Docker Engine
4. Git
5. Terraform
6. PostgreSQL
7. kubectl
8. ArgoCD
Minimum practical configuration
1. 4 CPU cores
2. 16 GB RAM
3. 50+ GB available SSD storage
Description
This course contains the use of artificial intelligence.
I only charge a fee solely for the time invested in building this comprehensive curriculum.
Stop Vibe Coding. Start Engineering Systems.
There is a major difference betweenmaking an application appear to work and engineering a platform that can survive reality.
A few Python scripts can generate GPS coordinates.
A dashboard can display vehicles on a map.
An API can return traffic information.
A Docker container can run a service.
But none of that, by itself, makes a production-grade transportation platform.
Real mobility infrastructure must deal with thousands of vehicles, unreliable networks, continuous telemetry, spatial constraints, public-transit schedules, security boundaries, infrastructure failures, observability
Requirements
, privacy regulations, deployment automation, and performance SLAs.That is where Vibe Coding ends—and engineering begins.
This course is built around that distinction.
You will not simply watch someone build a smart-city demo.
You will progressivelybuild the engineering foundations behind one.
From First GPS Packet to Smart-City Platform
The journey contains100 hands-on labs, deliberately structured to move from foundational infrastructure to advanced distributed-systems architecture.
You start locally.
Then you add spatial intelligence.
Then real-time streaming.
Then edge computing.
Then public transportation.
Then fleet optimization.
Then security.
Then observability.
Then cloud-native automation.
Finally, everything converges into one enterprise-scale capstone.
What's Inside?
Module 1 — Build the Foundation
Start by creating your own mobility engineering environment.
You will work with Docker, Git, PostgreSQL, PostGIS, MQTT, Python-generated GPS telemetry, spatial queries, GeoJSON, and your first end-to-end mobility pipeline.
The objective isn't memorizing commands.
It is understanding how the individual building blocks communicate.
Module 2 — Give Your Platform Spatial Intelligence
Transportation is inherently geographic.
You will import OpenStreetMap road networks, create spatial indexes, transform coordinate systems, calculate routes with pgRouting, publish geographic layers through GeoServer, create geofences, and visualize fleet assets in the browser.
You move fromlatitude/longitude data to actual spatial engineering.
Module 3 — Engineer Real-Time Streaming
Mobility systems generate events continuously.
You will build Kafka producers and consumers, process vehicle telemetry streams, handle schema evolution, work with consumer groups, persist time-series data, configure Kafka Connect, and monitor streaming lag.
This is where the platform begins behaving like a real distributed system.
Module 4 — Push Intelligence to the Edge
Vehicles and roadside devices cannot assume perfect connectivity.
You will secure MQTT, implement Last Will and Testament behavior, simulate edge nodes, manage device state, handle disconnections, deploy K3s, package services into containers, use Helm, and synchronize edge data with centralized infrastructure.
You will learn whyedge reliability is fundamentally different from ordinary web application reliability.
Module 5 — Connect Public Transportation
Now the platform understands buses and scheduled transit.
You will ingest static GTFS data and GTFS-Realtime feeds, process vehicle positions and trip updates, calculate schedule adherence, analyze historical delays, and build real-time transit alerting.
You are no longer building a generic IoT system.
You are building transportation intelligence.
Module 6 — Optimize Fleet Operations
Next comes operational optimization.
You will implement vehicle clustering, dynamic route recalculation, driver-status tracking, fuel-consumption models, harsh-braking and speeding alerts, multi-depot routing, OR-Tools optimization, fleet heatmaps, predictive maintenance triggers, and a comprehensive fleet API.
The goal is simple
Turn raw telemetry into operational decisions.
Module 7 — Make the Platform Secure
A mobility platform handles valuable operational information and potentially sensitive passenger data.
You will implement Keycloak, OAuth2/OIDC, JWT authentication, database RBAC, PII encryption, Kubernetes network policies, container vulnerability scanning, GDPR-oriented anonymization, audit logging, and HashiCorp Vault.
You will also perform a baseline security assessment.
Security is treated as architecture—not a final checkbox.
Module 8 — Engineer Reliability
Production systems fail.
Nodes disappear.
Networks break.
Consumers fall behind.
Services become unhealthy.
The engineering question is not whether failure happens.
It ishow your system responds when it does.
You will build observability with Prometheus, Grafana, Loki, and OpenTelemetry; configure Alertmanager; simulate failures; implement circuit breakers; configure Kubernetes autoscaling; and build automated health checks.
Module 9 — Automate the Infrastructure
Manual infrastructure does not scale.
You will use Terraform, managed Kubernetes concepts, DNS, certificates, ingress, persistent storage, ArgoCD GitOps, CI pipelines, blue-green deployments, automated database migrations, and disaster recovery workflows.
The platform becomes repeatable.
Deployable.
Recoverable.
Operational.
Module 10 — Enter the Capstone
The final module brings the entire architecture together.
You will work with multi-region availability, edge-to-cloud federation, AI-driven traffic prediction, traffic-signal APIs, AI governance considerations, load testing, database optimization, streaming-performance tuning, documentation, and operational runbooks.
And then comes the centerpiece.
Lab 100 — The PhD-Level Capstone
You will architect, deploy, stress, secure, observe, and validate anEnterprise Smart City Mobility Platform.
The system will ingest simulated telemetry from thousands of connected vehicles and public-transit sources.
It will process spatial constraints.
It will stream events through Kafka.
It will communicate with edge systems through MQTT.
It will use PostGIS and pgRouting for spatial intelligence.
It will persist time-series information.
It will enforce authentication and authorization.
It will apply privacy controls.
It will expose operational dashboards.
It will be monitored through Prometheus, Grafana, Loki, and OpenTelemetry.
And it will be deployed using modern cloud-native infrastructure and GitOps practices.
But the capstone is not evaluated simply because the application "works."
You will evaluate it against engineering criteria
-Resilience: Can the platform survive simulated component failures?
-Performance: Can it target sub-200 ms processing latency with 10,000 concurrent vehicle streams?
-Security: Are unauthenticated endpoints rejected and JWT controls enforced?
-Privacy: Are GDPR-oriented anonymization processes auditable?
-Observability: Can operators see stream lag, system health, failures, and spatial density?
-Operations: Can another engineer understand, deploy, troubleshoot, and recover the system?
That is the difference between a portfolio demo and an engineering project.
Your End Result
You will finish with more than 100 completed labs.
You will have built a connected body of practical experience across
IoT → MQTT → Kafka → GIS → PostGIS → GTFS → Fleet Optimization → Kubernetes → Security → Observability → Terraform → GitOps → AI → Distributed Systems
The objective is not to make you memorize 100 technologies.
The objective is to teach you how those technologiescompose into a resilient transportation platform.
Why Enroll Now?
The transportation industry is moving toward connected fleets, intelligent infrastructure, real-time analytics, edge computing, autonomous systems, and cloud-native operations.
The engineers who can connect these disciplines are far more valuable than engineers who understand only one isolated tool.
If you want another course that stops at CRUD APIs and dashboards, this is not it.
If you want to learn how toarchitect, deploy, secure, observe, optimize, and validate production-grade smart mobility infrastructure, you are in the right place.
Start with Lab 1. Build the foundation. Follow the architecture. Break the system. Fix it. Scale it. And finish with Lab 100.
Your smart-mobility engineering journey starts now.
Who this course is for
1. The Aspiring Smart Mobility / IoT Engineer
You understand Python, APIs, or basic cloud engineering but want to move into transportation technology.
2. The Backend, Cloud & Data Engineer
You already build software but want to escape isolated applications and understand distributed mobility systems.
3. The Senior Engineer Building Sovereign Infrastructure
You are responsible for architecture, platform engineering, smart-city infrastructure, fleet systems, or digital transformation and need production-grade patterns rather than another collection of API tutorials.
Homepage
https://www.udemy.com/course/smart-mobility-engineering-100-production-grade-labs/
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