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Learning path

Edge Computing learning path: everything to study, topic by topic

Comprehensive Edge Computing assessment covering cloud-to-edge continuum, architecture patterns, CDN and edge delivery, serverless edge functions, edge AI/ML inference, containerization at the edge, stream processing, edge security, 5G/MEC, hybrid architectures, and industrial IoT use cases.

Short answer

To learn Edge Computing, work through 6 modules and 22 topics in the order they build on each other. It starts with Edge Computing Fundamentals, then moves on to Edge Architecture & Delivery and Edge Platforms & Orchestration. Use the map as a checklist, or measure your level and study only what is missing.

6
modules
22
topics
45
min to assess

What you will study

Topics are grouped by module. Open one to see its objectives and key concepts.

1 · Edge Computing Fundamentals4 topics
  • Cloud vs Edge vs Fog

    Understanding the compute continuum from centralized cloud to edge and fog nodes, key differences in latency, bandwidth, data sovereignty, and when to use each model

    Objectives

    • Distinguish cloud, edge, and fog computing models
    • Identify when edge processing is preferred over cloud

    Key concepts

    • cloud computing
    • edge computing
    • fog computing
    • compute continuum
    • latency trade-offs
    • bandwidth savings
    • data sovereignty
    • edge node types
  • Fog Computing & Mist Computing

    Fog computing as an intermediate layer between edge devices and cloud, mist computing at the device level, OpenFog reference architecture, fog node capabilities

    Objectives

    • Explain the fog computing layer and its role
    • Differentiate fog from mist and edge computing

    Key concepts

    • fog layer
    • mist computing
    • OpenFog architecture
    • fog node capabilities
    • hierarchical processing
    • data aggregation
    • fog-cloud interaction
    • resource-constrained nodes
  • Edge Use Cases & Benefits

    Real-world edge computing scenarios including IoT, autonomous vehicles, smart retail, healthcare monitoring, AR/VR, gaming, video analytics, and content personalization

    Objectives

    • Map business scenarios to edge computing benefits
    • Evaluate trade-offs of edge vs cloud for specific use cases

    Key concepts

    • IoT edge processing
    • autonomous vehicles
    • smart retail
    • healthcare monitoring
    • AR/VR at edge
    • video analytics
    • content personalization
    • real-time decisioning
  • Latency Optimization

    Measuring and optimizing latency in edge deployments, round-trip time analysis, caching strategies, request routing, geographic proximity, and performance budgets

    Objectives

    • Measure and analyze edge latency metrics
    • Apply optimization techniques to reduce end-user latency

    Key concepts

    • round-trip time (RTT)
    • P50/P99 latency
    • geographic routing
    • caching at edge
    • performance budgets
    • time to first byte (TTFB)
    • connection reuse
    • protocol optimization
2 · Edge Architecture & Delivery4 topics
  • Edge Architecture Patterns

    Common edge architecture patterns including hub-and-spoke, mesh, hierarchical, peer-to-peer edge, single-tier vs multi-tier edge, and gateway aggregation patterns

    Objectives

    • Select appropriate edge architecture patterns for given requirements
    • Design multi-tier edge topologies

    Key concepts

    • hub-and-spoke topology
    • mesh edge topology
    • hierarchical edge
    • gateway aggregation
    • single-tier edge
    • multi-tier edge
    • edge gateway
    • peer-to-peer edge
  • CDN & Edge Delivery

    Content Delivery Networks as edge infrastructure, Cloudflare Workers, AWS CloudFront, Fastly Compute, cache invalidation strategies, origin shielding, edge-side includes

    Objectives

    • Configure CDN for optimal edge delivery
    • Implement cache invalidation strategies at edge

    Key concepts

    • CDN architecture
    • Cloudflare Workers
    • AWS CloudFront
    • Fastly Compute
    • cache invalidation
    • origin shielding
    • edge-side includes (ESI)
    • PoP (Point of Presence)
  • Serverless Edge Functions

    Running serverless functions at edge locations, Cloudflare Workers, Deno Deploy, Vercel Edge Functions, AWS Lambda@Edge, cold start optimization, runtime constraints

    Objectives

    • Build and deploy serverless functions at the edge
    • Optimize cold start times and manage runtime constraints

    Key concepts

    • Cloudflare Workers runtime
    • Deno Deploy
    • Vercel Edge Functions
    • Lambda@Edge
    • cold start optimization
    • V8 isolates
    • runtime size limits
    • edge function lifecycle
  • Edge-Cloud Hybrid Architectures

    Designing systems that span edge and cloud, data synchronization, eventual consistency, workload placement decisions, split processing, and cloud fallback strategies

    Objectives

    • Design hybrid edge-cloud architectures with appropriate data sync
    • Make workload placement decisions based on latency and cost

    Key concepts

    • hybrid architecture
    • workload placement
    • data synchronization
    • eventual consistency
    • split processing
    • cloud fallback
    • edge-to-cloud pipeline
    • federated management
3 · Edge Platforms & Orchestration3 topics
  • Containers at the Edge

    Running containers on resource-constrained edge nodes, K3s, MicroK8s, K0s, container image optimization, registry mirroring, air-gapped deployments

    Objectives

    • Deploy containerized workloads on edge nodes with K3s or MicroK8s
    • Optimize container images for constrained environments

    Key concepts

    • K3s
    • MicroK8s
    • K0s
    • container image optimization
    • registry mirroring
    • air-gapped deployment
    • resource constraints
    • lightweight runtimes
  • Edge Orchestration

    Managing and orchestrating workloads across distributed edge nodes, fleet management, GitOps for edge, rolling updates, KubeEdge, Azure IoT Edge, AWS Greengrass

    Objectives

    • Orchestrate container workloads across edge fleets
    • Implement GitOps workflows for edge deployments

    Key concepts

    • fleet management
    • GitOps for edge
    • KubeEdge
    • Azure IoT Edge
    • AWS Greengrass
    • rolling updates at edge
    • edge device lifecycle
    • remote management
  • Edge AI/ML Inference

    Running machine learning models at the edge, model optimization (quantization, pruning, distillation), TensorFlow Lite, ONNX Runtime, NVIDIA Jetson, inference accelerators

    Objectives

    • Deploy optimized ML models for edge inference
    • Choose appropriate model compression techniques

    Key concepts

    • model quantization
    • model pruning
    • knowledge distillation
    • TensorFlow Lite
    • ONNX Runtime
    • NVIDIA Jetson
    • inference accelerators (TPU/NPU)
    • edge model lifecycle
4 · Edge Data Processing3 topics
  • Stream Processing at Edge

    Real-time stream processing on edge nodes, Apache Kafka at edge, MQTT-based pipelines, windowing, event-driven architectures, CEP (Complex Event Processing) at edge

    Objectives

    • Design real-time stream processing pipelines at the edge
    • Apply windowing and CEP patterns for edge analytics

    Key concepts

    • edge stream processing
    • MQTT protocol
    • Kafka at edge
    • event windowing
    • complex event processing
    • event-driven edge
    • backpressure handling
    • time-series ingestion
  • Edge Databases & Storage

    Embedded databases for edge (SQLite, LevelDB, RocksDB), edge-native databases, CRDTs for conflict resolution, sync protocols, local-first architectures

    Objectives

    • Select appropriate databases for edge deployments
    • Implement conflict resolution with CRDTs for distributed edge data

    Key concepts

    • SQLite at edge
    • LevelDB/RocksDB
    • CRDTs
    • conflict resolution
    • sync protocols
    • local-first architecture
    • edge data retention
    • embedded key-value stores
  • Data Filtering & Aggregation

    Reducing data volume at the edge through filtering, aggregation, compression, deduplication, sampling strategies, and selective cloud upload

    Objectives

    • Design data reduction pipelines at the edge
    • Apply filtering and aggregation to minimize cloud transfer costs

    Key concepts

    • edge filtering
    • data aggregation
    • compression at edge
    • deduplication
    • sampling strategies
    • selective upload
    • data tiering
    • bandwidth optimization
5 · Edge Security & Networking5 topics
  • Edge Security & Zero Trust

    Securing edge infrastructure with zero-trust principles, mTLS, edge firewalls, secure boot, hardware security modules (HSM), threat modeling for distributed edge

    Objectives

    • Design zero-trust security for edge deployments
    • Identify and mitigate edge-specific threat vectors

    Key concepts

    • zero-trust architecture
    • mTLS at edge
    • edge firewalls
    • secure boot
    • hardware security modules
    • threat modeling
    • edge attack surface
    • data-in-transit encryption
  • Device Identity & Attestation

    Device identity management, X.509 certificates, device attestation, TPM (Trusted Platform Module), device provisioning, certificate rotation at scale

    Objectives

    • Implement device identity and attestation at scale
    • Design certificate lifecycle management for edge fleets

    Key concepts

    • device identity
    • X.509 certificates
    • device attestation
    • TPM
    • device provisioning
    • certificate rotation
    • PKI for edge
    • identity lifecycle
  • Edge Networking & SD-WAN

    Software-defined WAN for edge connectivity, network segmentation, traffic prioritization, multi-path routing, WAN optimization, edge routing protocols

    Objectives

    • Design SD-WAN architectures for edge connectivity
    • Implement traffic prioritization and multi-path routing

    Key concepts

    • SD-WAN
    • network segmentation
    • traffic prioritization
    • multi-path routing
    • WAN optimization
    • edge routing
    • overlay networks
    • network policy enforcement
  • 5G & Multi-access Edge Computing

    5G network slicing for edge workloads, MEC (Multi-access Edge Computing) standards, ETSI MEC framework, ultra-reliable low-latency communication (URLLC), mobile edge apps

    Objectives

    • Design applications leveraging 5G MEC infrastructure
    • Apply ETSI MEC framework to real-world deployments

    Key concepts

    • 5G network slicing
    • MEC standards
    • ETSI MEC framework
    • URLLC
    • mobile edge applications
    • edge application server
    • service mesh at edge
    • radio access network (RAN)
  • Edge Observability

    Monitoring distributed edge infrastructure, distributed tracing at edge, metrics aggregation, log shipping from edge, alerting for intermittent connectivity

    Objectives

    • Design observability strategies for distributed edge systems
    • Handle monitoring challenges from intermittent connectivity

    Key concepts

    • edge monitoring
    • distributed tracing
    • metrics aggregation
    • log shipping
    • intermittent connectivity
    • edge health checks
    • SLA monitoring
    • observability pipeline
6 · Advanced Edge Topics3 topics
  • Industrial Edge & IIoT

    Industrial IoT edge computing, OPC-UA, SCADA integration, digital twins, predictive maintenance, real-time control systems, safety-critical edge

    Objectives

    • Design edge computing solutions for industrial environments
    • Integrate OPC-UA and SCADA with modern edge platforms

    Key concepts

    • Industrial IoT (IIoT)
    • OPC-UA protocol
    • SCADA integration
    • digital twins
    • predictive maintenance
    • real-time control
    • safety-critical systems
    • industry 4.0
  • Edge Reliability & Resilience

    Designing resilient edge systems, offline-first patterns, graceful degradation, self-healing edge nodes, chaos engineering at edge, disaster recovery for edge sites

    Objectives

    • Design self-healing and resilient edge architectures
    • Implement offline-first patterns for edge autonomy

    Key concepts

    • offline-first patterns
    • graceful degradation
    • self-healing nodes
    • chaos engineering at edge
    • edge disaster recovery
    • autonomous operation
    • partition tolerance
    • edge failover strategies
  • Edge Cost & Governance

    Total cost of ownership for edge deployments, edge resource budgeting, governance models, compliance at edge, multi-tenant edge, edge capacity planning, vendor strategies

    Objectives

    • Calculate and optimize total cost of ownership for edge deployments
    • Design governance and compliance frameworks for distributed edge

    Key concepts

    • edge TCO
    • resource budgeting
    • governance models
    • compliance at edge
    • multi-tenant edge
    • capacity planning
    • vendor strategy
    • edge ROI analysis

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Frequently asked questions

Where should I start studying Edge Computing?

Start with Edge Computing Fundamentals: Cloud vs Edge vs Fog, Fog Computing & Mist Computing, and Edge Use Cases & Benefits. The map orders topics by what they depend on, so each one prepares the next.

How long does a full Edge Computing assessment take?

A full assessment of this path takes about 45 minutes. Study time depends on where you start; a diagnostic shows which topics you can skip.

Do I need an account to use this page?

No. The map, objectives and key concepts are open. You only need an account to be measured, get your gap by topic and receive a plan.