Demonstration applications using Hazelcast Jet
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README.md
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README.md

Hazelcast Jet Demo Applications

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

Application Demos

  • Real-time Image Recognition - Recognizes images present in the webcam video input with a model trained with CIFAR-10 dataset.
  • Twitter Cryptocurrency Sentiment Analysis - Twitter content is analyzed in real time to calculate cryptocurrency trend list with popularity index.
  • Real-Time Road Traffic Analysis and Prediction - Continuously computes linear regression models from current traffic. Uses the trend from week ago to predict traffic now.
  • Real-time Sports Betting Engine - 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.
  • 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.
  • Market Data Ingestion - 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.
  • Markov Chain Generator Generates a Markov Chain with probabilities based on supplied classical books.

External Demos

  • 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.

Prerequisites

  • 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