How Big Data is Powering the Internet of Things (IoT) Revolution
summary
Claims that Big Data pipelines make IoT analytics practical by combining batch and real-time layers; demo builds a Python faker producer, sends simulated sensor events through Amazon Kinesis + S3, processes them with Spark Structured Streaming on Databricks, and visualizes results. Uses 100 simulated warehouse devices, two-hour tumbling windows, finds 28 devices in “red” CO2 range and some countries with max temps above 90°F (Philippines spikes), and surfaces batteries with level 2/1 (three devices at level 2). Streaming runs live, shows input vs processing lag and is used to trigger operational alerts.
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chapters (44)
- 0:00Intro
- 0:18Aidas background
- 1:18Aidas work
- 2:11Overview
- 2:43IoT Devices
- 3:44What is IoT
- 4:32Benefits of IoT
- 5:59Challenges of IoT
- 8:08White Cloud
- 10:55IoT Helix
- 12:01Indoor Air Quality Monitoring
- 12:17Indoor Air Quality Monitoring Example
- 13:30Analysis
- 14:33Architecture
- 15:43Sending Data
- 17:02ingestion and storage
- 17:17data generation
- 17:56data analytics
- 18:09data bricks
- 18:34data description
- 19:18batch processing
- 20:02big files
- 20:39read data
- 21:25sample data
- 22:11data continuity
- 23:13data analysis
- 26:56streaming processing
- 27:06streaming
- 27:58streaming vs batch
- 28:45input vs processing rate
- 29:51how a stream works
- 30:25low battery level
- 30:36even vs received time
- 31:34maximum day
- 31:45latest event
- 32:13reading to memory
- 32:48creating queries
- 33:22battery levels
- 35:41group data example
- 36:11query
- 37:33high levels
- 38:10active queries
- 38:37historical batch
- 39:31questions
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