Demonstration applications using Hazelcast Jet
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
flight-telemetry Fix typo Nov 14, 2018
jetleopard Upgrade Jackson dependency (#73) Jan 7, 2019
market-data-ingest Update Jet version to 0.7 (#71) Oct 16, 2018
markov-chain-generator Update Jet version to 0.7 (#71) Oct 16, 2018
realtime-image-recognition Added prerequisites section Nov 2, 2018
road-traffic-predictor Update Jet version to 0.7 (#71) Oct 16, 2018
.gitattributes Initial commit Feb 5, 2018
.gitignore Precious Metals example (#47) Feb 19, 2018
LICENSE Initial commit Feb 5, 2018
pom.xml Update Jet version to 0.7.2 (#74) Jan 7, 2019

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

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.


  • 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