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The course first defines IoT and why IoT data processing is very different from typical big data analytics, with its unique requirements for data security, device identity, huge data volume, and real-time processing. The course reviews the challenges and current architectures of IoT data collection to the cloud. Using a hands-on approach in Amazon Web Services (AWS) with simulated data, you will learn to build a messaging and data streaming system with Apache Spark, Storm and Kafka. You will learn to perform a real-time pattern analysis with IoT data, utilizing the Hadoop ecosystem and gaining further predictive insights to set up actionable triggers for business and data mining applications.
You will use AWS accounts to work on assignments that let you design and analyze your own IoT device data and explore valuable insights. The course demonstrates data flow and offers hands-on experience gaining business intelligence from IoT big data. The instructor will share industrial practices of IoT big data processing and analytics. The course focuses on how to use tools and provides a basic overview; in-depth of the data processing tools and frameworks are covered in other courses. Some programming will be needed to customize the data flow at the Hbase layer in Spark.
At the conclusion of the course, you should be able to
- Describe characteristics and requirements of IoT specific data
- Demonstrate how to build a data flow to connect an IoT system or device data to the cloud in specific formats
- Explain how to use big data tools to process IoT data in distributed computing
- Employ algorithms (including Kafka data stream processing and machine learning) to analyze IoT data patterns and extract intelligence
Skills Needed: Software installation and some programming experience in C, Java or Python (one of the three) is required.
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