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Artificial Intelligence for IoT Cookbook

You're reading from   Artificial Intelligence for IoT Cookbook Over 70 recipes for building AI solutions for smart homes, industrial IoT, and smart cities

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781838981983
Length 260 pages
Edition 1st Edition
Languages
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Author (1):
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Michael Roshak Michael Roshak
Author Profile Icon Michael Roshak
Michael Roshak
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Table of Contents (11) Chapters Close

Preface 1. Setting Up the IoT and AI Environment 2. Handling Data FREE CHAPTER 3. Machine Learning for IoT 4. Deep Learning for Predictive Maintenance 5. Anomaly Detection 6. Computer Vision 7. NLP and Bots for Self-Ordering Kiosks 8. Optimizing with Microcontrollers and Pipelines 9. Deploying to the Edge 10. About Packt

Enriching data using Kafka's KStreams and KTables

Often, in IoT, there are external data sources that must be included. This could be weather data that affects the performance of the device or data from other nearby devices. An easy way of doing this is to use Kafka KSQL Server. Like we did in the previous recipe, we are going to use Confluent Cloud's KSQL Server, which you can get if you have a Confluent Cloud subscription.

In this recipe, we are going to get data from a weather service topic and put it into a KTable. A KTable is similar to a database table. All data coming into Kafka comes in key-value pairs. With KTable, when data comes in with a new key, we insert it into our KTable. If it contains a key that already exists in our KTable, we update it. We are also going to convert our topic into a KStream. This allows us to run standard SQL-like queries over our table and stream. By doing this, we can, for example, query the current weather and join it to the engine data...

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