15 Topics
10 Days
71 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers
- IoT Developers
- Data Analysts
- Big Data Architects
- System Administrators
- Cloud Engineers
Session Objectives
- Understand the fundamentals of Big Data and the Internet of Things (IoT).
- Master the integration of IoT data with Big Data platforms.
- Utilize data ingestion techniques for IoT data streams.
- Implement stream processing for real-time IoT data analysis.
- Design scalable and efficient IoT data architectures.
- Utilize cloud-based IoT and Big Data services.
- Troubleshoot and optimize IoT data pipelines.
- Implement data security and access control for IoT data.
- Integrate IoT data with analytics and visualization tools.
- Understand how to monitor and manage IoT data systems.
- Explore advanced IoT data processing techniques.
- Apply real world use cases for IoT data integration.
- Leverage machine learning for IoT data analysis.
About the Course
Harness high-velocity sensor feeds and telemetry streams with our Big Data and the Internet of Things (IoT) Training Course. Tailored for data engineers, IoT developers, and solutions architects, this hands-on program equips technical teams to ingest, process, and analyze massive volumes of connected-device data. Guided by IoT data architects, you will master real-time event streaming, edge-to-cloud data ingestion protocols (MQTT, Kafka), time-series database management, and anomaly detection algorithms—enabling your organization to transform raw device telemetry into predictive operational insights and scalable industrial IoT solutions.
Curriculum & Topics
15 Topics | 71 Sessions
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Workshop 1.1:
Fundamentals of Big Data and IoT.
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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 integration environment.
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Workshop 1.4:
Introduction to IoT and Big Data tools and frameworks.
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Workshop 1.5:
Best practices for IoT data integration.
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Workshop 2.1:
Utilizing message brokers (MQTT, Kafka) for IoT data ingestion.
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Workshop 2.2:
Implementing data ingestion from various IoT devices.
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Workshop 2.3:
Utilizing edge computing for data preprocessing.
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Workshop 2.4:
Implementing data serialization and deserialization.
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Workshop 2.5:
Best practices for data ingestion.
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Workshop 3.1:
Implementing stream processing engines (Flink, Spark Streaming).
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Workshop 3.2:
Utilizing real-time data analytics for IoT streams.
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Workshop 3.3:
Implementing data filtering and aggregation.
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Workshop 3.4:
Building real-time dashboards for IoT data.
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Workshop 3.5:
Best practices for stream processing.
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Workshop 4.1:
Designing scalable IoT data architectures.
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Workshop 4.2:
Utilizing distributed data storage and processing.
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Workshop 4.3:
Implementing cloud-based IoT data solutions.
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Workshop 4.4:
Optimizing data pipelines for performance.
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Workshop 4.5:
Best practices for scalable architectures.
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Workshop 5.1:
Utilizing AWS IoT and Big Data services.
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Workshop 5.2:
Utilizing Azure IoT and Big Data services.
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Workshop 5.3:
Utilizing Google Cloud IoT and Big Data services.
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Workshop 5.4:
Implementing serverless IoT data processing.
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Workshop 5.5:
Best practices for cloud services.
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Workshop 6.1:
Debugging IoT data pipelines.
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Workshop 6.2:
Analyzing data flow and performance.
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Workshop 6.3:
Utilizing monitoring tools and techniques.
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Workshop 6.4:
Optimizing data processing and storage.
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Workshop 6.5:
Best practices for troubleshooting.
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Workshop 7.1:
Implementing data security for IoT data.
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Workshop 7.2:
Utilizing authentication and authorization.
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Workshop 7.3:
Implementing data encryption and masking.
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Workshop 7.4:
Implementing data auditing and compliance.
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Workshop 7.5:
Best practices for data security.
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Workshop 8.1:
Integrating IoT data with analytics tools (Tableau, Power BI).
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Workshop 8.2:
Utilizing data visualization for IoT data insights.
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Workshop 8.3:
Implementing interactive dashboards for IoT data.
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Workshop 8.4:
Best practices for analytics integration.
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Workshop 9.1:
Monitoring IoT data systems and pipelines.
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Workshop 9.2:
Implementing alerting and notifications.
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Workshop 9.3:
Utilizing monitoring tools and techniques.
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Workshop 9.4:
Managing IoT data infrastructure.
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Workshop 9.5:
Best practices for monitoring.
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Workshop 10.1:
Implementing time series analysis for IoT data.
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Workshop 10.2:
Utilizing geospatial data processing for IoT.
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Workshop 10.3:
Implementing complex event processing (CEP).
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Workshop 10.4:
Advanced techniques for IoT data analysis.
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Workshop 10.5:
Best practices for advanced processing.
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Workshop 11.1:
Implementing IoT data for smart cities.
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Workshop 11.2:
Utilizing IoT data for industrial automation.
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Workshop 11.3:
Implementing IoT data for healthcare applications.
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Workshop 11.4:
Utilizing IoT data for environmental monitoring.
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Workshop 11.5:
Best practices for real world applications.
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Workshop 12.1:
Utilizing machine learning for IoT data analysis.
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Workshop 12.2:
Implementing anomaly detection for IoT data.
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Workshop 12.3:
Implementing predictive maintenance for IoT devices.
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Workshop 12.4:
Building machine learning models for IoT data.
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Workshop 12.5:
Best practices for machine learning.
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Workshop 13.1:
Deploying IoT data solutions on cloud platforms.
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Workshop 13.2:
Utilizing cloud-based IoT data services.
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Workshop 13.3:
Optimizing cloud resources for IoT data.
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Workshop 13.4:
Best practices for cloud deployment.
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Workshop 14.1:
Implementing data governance policies for IoT data.
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Workshop 14.2:
Utilizing metadata management for IoT data.
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Workshop 14.3:
Implementing data lineage and data dictionary.
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Workshop 14.4:
Best practices for data governance.
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Workshop 15.1:
Emerging trends in IoT data integration.
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Workshop 15.2:
Utilizing AI and automation in IoT data workflows.
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Workshop 15.3:
Implementing edge computing for IoT data.
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Workshop 15.4:
Best practices for future IoT data.
