Live Build Of A Python Service That Tracks, Transforms & Delivers Sensor Data In Real Time
summary
Builds a Python streaming pipeline with Quix, Kafka, Kubernetes, Docker and Helm that reads phone IoT GPS/accelerometer data, computes a new total G‑force column, and deploys separate pop/sub microservices via Git to scale horizontally. Adds a library threshold detector set to 15g threshold and sends alerts to Slack; deployment uses Docker images and Helm charts and succeeds, with claimed persistence and fault tolerance and about 40 ms round trip from phone to cloud and back.
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chapters (11)
- 0:00Introduction to streaming
- 1:23Streaming vs batch processing
- 3:33Pub/sub service concept
- 7:54Live demo setup
- 9:39Data quality assessment
- 10:44Building first microservice
- 13:16Deploying to production
- 14:40Adding crash detection
- 16:05Sending alerts to Slack
- 17:54Scaling and performance
- 18:15Q&A and resources
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