# hazelcast-jet-demos

**Repository Path**: porter4code/hazelcast-jet-demos

## Basic Information

- **Project Name**: hazelcast-jet-demos
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-09
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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

* [Bitcoin Death Cross](./bitcoin-death-cross) - Predicting 2018's downward trend for Bitcoin.
* [Real-time Image Recognition](./realtime-image-recognition) - Recognizes images present in the webcam video input with a model trained with CIFAR-10 dataset.
* [Twitter Cryptocurrency Sentiment Analysis](./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](./road-traffic-predictor) - Continuously computes linear regression models from 
current traffic. Uses the trend from week ago to predict traffic now.
* [Real-time Sports Betting Engine](./jetleopard) - 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](./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](./market-data-ingest) - 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](./markov-chain-generator) Generates a Markov Chain with probabilities based on supplied classical books.
* [Train Track](./train-track) An [Apache Beam](https://beam.apache.org/) IOT example, tracking a GPS feed from a train.
* [TensorFlow](./tensorflow) A [TensorFlow](https://www.tensorflow.org/) example showcasing a ML model execution in Hazelcast Jet pipelines.
* [Debezium Change Data Capture with Apache Kafka](./debezium-cdc-with-kafka) A [Debezium](http://www.debezium.io) example showcasing consumption of CDC events from Apache Kafka in Hazelcast Jet pipelines.
* [Debezium Change Data Capture without Apache Kafka](./debezium-cdc-without-kafka) A [Debezium](http://www.debezium.io) example showcasing consumption of CDC events directly from Debezium using Kafka Connect source in Hazelcast Jet pipelines.

## External Demos

* [Real-Time Trade Processing](https://github.com/oliversalmon/imcs-demo) 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. 
* [Transport Tycoon Collision Prevention](https://github.com/vladoschreiner/transport-tycoon-demo/) Vladimír Schreiner. Reads a vehicle telemetry data from the running [Open Transport Tycoon DeLuxe](https://www.openttd.org/) game and uses Jet to predict and prevent collisions.

## Prerequisites

- Git Large File Storage: [Installation Guide](https://git-lfs.github.com/)
  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](https://docs.oracle.com/javase/8/docs/technotes/guides/install/install_overview.html)
- Apache Maven: [Installation Guide](https://maven.apache.org/install.html)