README.md

Eagleeye

A realtime ingestion and profiling engine for fast data.

There are 3 docker-compose files:
docker-compose-service.yaml - this contains all the 4 services ( kafka, pinot, flink, superset)
docker-compose-initialize.yaml - this contains the logic to configure pinot tables listen to respective kafka topic for realtime ingestion.
docker-compose-invoke.yaml - this contains the dummy data generator (written in python) and the flink profiler job (written in java)

##Steps to run the project.

1. Build the services

docker-compose -f docker-compose-service.yaml build
docker-compose -f docker-compose-initialize.yaml build
docker-compose -f docker-compose-invoke.yaml build

2. Bring up the services (wait for 1-2 minutes in between while running the following 3 commands)

docker-compose -f docker-compose-service.yaml up -d
docker-compose -f docker-compose-initialize.yaml up -d
docker-compose -f docker-compose-invoke.yaml up -d

The services can be accessed from the web UI:
kafka - http://0.0.0.0:9999
pinot - http://0.0.0.0:9000
flink - http://0.0.0.0:8081
superset - http://0.0.0.0:8088

Configure superset dashboard

1. Create a Pinot database connection
   connection string - pinot://pinot-broker:8099/query/sql?controller=http://pinot-controller:9000/

2. Import dashboard - threathunt_dashboard.json

##To Bring down the services

docker-compose -f docker-compose-service.yaml down
docker-compose -f docker-compose-initialize.yaml down
docker-compose -f docker-compose-invoke.yaml down

A detailed video on how to run this project can be found here:-

See also Demo