15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers
- IoT Developers
- Systems Architects
- Cloud Engineers
- Embedded Systems Developers
- Data Scientists
- Anyone needing IoT data engineering skills
Session Objectives
- Understand the fundamentals of data engineering and IoT data pipelines.
- Master data ingestion and stream processing for IoT data.
- Utilize edge computing for real-time data analysis.
- Implement data storage and retrieval strategies for IoT data.
- Design and build scalable data pipeline architectures for IoT.
- Optimize data pipelines for low latency and high throughput.
- Troubleshoot and address common issues in IoT data pipeline implementations.
- Implement data security and privacy in IoT data pipelines.
- Integrate IoT data pipelines with cloud and on-premises systems.
- Understand how to handle large-scale IoT data processing.
- Explore advanced data engineering patterns for IoT (e.g., time-series databases, data analytics).
- Apply real world use cases for data engineering in IoT applications.
- Leverage data engineering tools and frameworks for efficient IoT data management.
About the Course
Harness high-velocity device streams with our Data Engineering and IoT Training Course. Tailored for data engineers, IoT developers, and systems architects, this hands-on program equips technical leads to design and scale resilient data pipelines for connected device networks. Guided by IoT data architects, you will master edge computing topologies, lightweight streaming protocols (MQTT/CoAP), high-throughput message brokers, stateful stream processing, and time-series storage design—enabling your organization to process massive telemetry payloads, eliminate latency, and drive real-time operational intelligence.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of data engineering and IoT data pipelines.
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Workshop 1.2:
Overview of IoT data characteristics and challenges.
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Workshop 1.3:
Setting up an IoT data engineering environment.
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Workshop 1.4:
Introduction to IoT data tools and frameworks.
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Workshop 1.5:
Best practices for IoT data engineering.
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Workshop 2.1:
Mastering data ingestion and stream processing for IoT data.
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Workshop 2.2:
Utilizing message queues and streaming platforms (Kafka, MQTT).
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Workshop 2.3:
Implementing real-time data ingestion and processing.
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Workshop 2.4:
Designing and building data ingestion pipelines.
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Workshop 2.5:
Best practices for IoT data ingestion.
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Workshop 3.1:
Utilizing edge computing for real-time data analysis.
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Workshop 3.2:
Implementing data processing at the edge devices.
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Workshop 3.3:
Designing and building edge data pipelines.
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Workshop 3.4:
Optimizing edge computing for latency reduction.
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Workshop 3.5:
Best practices for edge computing in IoT.
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Workshop 4.1:
Implementing data storage and retrieval strategies for IoT data.
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Workshop 4.2:
Utilizing time-series databases and NoSQL databases.
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Workshop 4.3:
Designing and building data storage solutions.
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Workshop 4.4:
Optimizing data retrieval for analytics.
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Workshop 4.5:
Best practices for IoT data storage.
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Workshop 5.1:
Designing and building scalable data pipeline architectures for IoT.
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Workshop 5.2:
Utilizing cloud-based data processing services.
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Workshop 5.3:
Implementing microservices architecture for data pipelines.
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Workshop 5.4:
Designing scalable data processing frameworks.
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Workshop 5.5:
Best practices for IoT pipeline architecture.
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Workshop 6.1:
Optimizing data pipelines for low latency and high throughput.
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Workshop 6.2:
Utilizing data compression and filtering techniques.
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Workshop 6.3:
Implementing pipeline optimization for real-time analytics.
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Workshop 6.4:
Designing efficient data processing workflows.
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Workshop 6.5:
Best practices for pipeline optimization.
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Workshop 7.1:
Troubleshooting and addressing common issues in IoT data pipeline implementations.
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Workshop 7.2:
Analyzing pipeline logs and error messages.
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Workshop 7.3:
Utilizing problem-solving techniques for resolution.
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Workshop 7.4:
Resolving common IoT data pipeline errors.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data security and privacy in IoT data pipelines.
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Workshop 8.2:
Utilizing data encryption and access control.
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Workshop 8.3:
Designing and building secure data pipelines.
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Workshop 8.4:
Optimizing security for IoT data protection.
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Workshop 8.5:
Best practices for IoT data security.
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Workshop 9.1:
Integrating IoT data pipelines with cloud and on-premises systems.
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Workshop 9.2:
Utilizing data integration tools and APIs.
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Workshop 9.3:
Implementing hybrid data processing solutions.
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Workshop 9.4:
Designing efficient data integration strategies.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle large-scale IoT data processing.
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Workshop 10.2:
Utilizing distributed data processing frameworks.
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Workshop 10.3:
Implementing data aggregation and analytics.
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Workshop 10.4:
Designing scalable data processing systems.
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Workshop 10.5:
Best practices for large scale IoT data.
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Workshop 11.1:
Exploring advanced data engineering patterns for IoT (time-series databases, data analytics).
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Workshop 11.2:
Utilizing time-series databases for IoT data storage.
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Workshop 11.3:
Implementing data analytics for real-time insights.
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Workshop 11.4:
Designing and building advanced data processing solutions.
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Workshop 11.5:
Optimizing advanced patterns for specific applications.
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Workshop 11.6:
Best practices for advanced patterns.
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Workshop 12.1:
Implementing data pipelines for smart city IoT data.
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Workshop 12.2:
Utilizing data engineering for industrial IoT (IIoT) data.
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Workshop 12.3:
Implementing data pipelines for healthcare IoT devices.
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Workshop 12.4:
Utilizing data engineering for environmental monitoring.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing data engineering tools and frameworks (Apache Kafka, AWS IoT Core).
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Workshop 13.2:
Implementing IoT data pipelines with specific tools.
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Workshop 13.3:
Designing and building automated data processing workflows.
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Workshop 13.4:
Optimizing tool usage for efficient data management.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing pipeline monitoring and metrics.
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Workshop 14.2:
Utilizing data processing metrics and logs.
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Workshop 14.3:
Designing and building data pipeline dashboards.
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Workshop 14.4:
Optimizing monitoring for real-time insights.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in data engineering for IoT.
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Workshop 15.2:
Utilizing AI for IoT data analytics and processing.
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Workshop 15.3:
Implementing serverless data pipelines for IoT.
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Workshop 15.4:
Best practices for future applications.
