Lesson 3: Industrial Data Pipelines, Historians, Cloud, Analytics, and Digital Twins

Lesson Overview

This lesson develops practical skills in industrial data pipelines, historians, cloud, analytics, and digital twins for the Industrial Internet of Things (IIoT) Fundamentals Training course, with implementation, verification, documentation, and troubleshooting guidance.

Topics Covered

Time-series pipelines, brokers, historians, databases, APIs, cloud ingestion, and retention
Asset hierarchies, information models, metadata, contextualization, calculated values, and lineage
Dashboards, KPIs, events, anomaly detection, predictive analytics, and machine-learning workflows
Digital-twin concepts, model synchronization, simulation, condition monitoring, and lifecycle data
Data completeness, reconciliation, governance, observability, performance, and analytics validation

Practical Activity

Create or review a practical configuration for time-series pipelines, brokers, historians, databases, apis, cloud ingestion, and retention.
Build and test an exercise involving asset hierarchies, information models, metadata, contextualization, calculated values, and lineage and dashboards, kpis, events, anomaly detection, predictive analytics, and machine-learning workflows.
Use diagnostics or evidence to validate data completeness, reconciliation, governance, observability, performance, and analytics validation.

Key Takeaways

Explain time-series pipelines, brokers, historians, databases, apis, cloud ingestion, and retention.
Apply asset hierarchies, information models, metadata, contextualization, calculated values, and lineage and dashboards, kpis, events, anomaly detection, predictive analytics, and machine-learning workflows.
Validate and troubleshoot data completeness, reconciliation, governance, observability, performance, and analytics validation.

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Industrial Internet of Things (IIoT) Fundamentals Training 1 Section4 Lessons32 Hours

Curriculum