These are Demonstration applications using Hazelcast Jet. Each is a full application and demonstrates how you can use Jet to solve real-world problems.
For smaller, feature specific samples see https://github.com/hazelcast/hazelcast-jet-code-samples
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Real-time Image Recognition - Recognizes images present in the webcam video input with a model trained with CIFAR-10 dataset.
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Cryptocurrency Realtime Trend - Twitter content is analyzed in real time to calculate cryptocurrency trend list with popularity index.
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Online Training Traffic Predictor - Continuously computes linear regression models from current traffic. Uses the trend from week ago to predict traffic now.
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Jet Leopard - This is a simple example of a sports book and is a good introduction to the Pipeline API. It also uses Hazelcast IMDG as an in-memory data store.
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Flight Telemetry - Reads a stream of telemetry data from ADB-S on all commercial aircraft flying anywhere in the world. There is typically 5,000 - 6,000 aircraft at any point in time. This is then filtered, aggregated and certain features are enriched and displayed in Grafana.
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Market Data Distributor - Uploads a stream of stock market data (prices) from a Kafka topic into an IMDG map. Data is analysed as part of the upload process, calculating the moving averages to detect buy/sell indicators. Input data here is manufactured to ensure such indicators exist, but this is easy to reconnect to real input.
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Markov Chain Generator Generates a Markov Chain with probabilities based on supplied classical books.
- Real-Time Trade Processing Oliver Buckley-Salmon. Reads from a Kafka topic with Jet and then storage to HBase and Hazelcast IMDG. Shows enrichment and streaming aggregations. Jet 0.4.
- Git Large File Storage: Installation Guide Some of the demo applications includes machine learning models in their use cases. Since some models' size exceeds GitHub's 100MB file storage limit this repository uses Git LFS.
- Java Development Kit 8+: Installation Guide
- Apache Maven: Installation Guide