Machine learning & Kafka KSQL stream processing — bug me when I’ve left the heater on

This page summarizes the projects mentioned and recommended in the original post on dev.to

InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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  • stream-smarts

    Real-time anomaly detection using Kafka, KSQL User Defined Function and a pre-trained model

  • A bit of python code consumes the ANOMOLY_POWER topic and calls pushbullet. A consumer is established, and an event handler calls the notification service on receipt of a new Kafka events. Each message generates a new push notification.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • ksql-udf-deep-learning-mqtt-iot

    Deep Learning UDF for KSQL for Streaming Anomaly Detection of MQTT IoT Sensor Data

  • Inspiration for this project comes from Kai Waehner and his project Deep Learning UDF for KSQL. The notification system was inspired by Robin Moffatt and his blog on Event-Driven Alerting with Slack.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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