A platform for planning and scheduling data analyses and visualisations. Data analyses can then be configured in order to run periodically, and visualisations will be updated in realtime, and can be embedded everywhere on the web.
The platform is organized with docker-compose, so the installation is quite simple:
- Clone the repo:
git clone https://github.com/fablabbcn/DataVisPlanner.git - Go inside the project folder:
cd DataVisPlanner - Customise the
docker-compose.ymlfile if necessary - Customise the
environment.envandairflow.envfiles if necessary - Customise the
docker-compose.ymlandmongo/mongo-init.jsfiles for securing MongoDB where:- MONGOADMINUSERNAME is the admin username for MongoDB
- MONGOADMINPASSWORD is the admin password for MongoDB
- MONGOUSERNAME is the admin username for connecting Meteor with MongoDB
- MONGOPASSWORD is the admin password for connecting Meteor with MongoDB
- Copy the
environment.envto.envfile - Test the platform:
docker-compose up --build - Run the platform:
docker-compose up -d
The DataVisPlanner platform is based on several docker containers, only some of them need to be accessed directly. They can be accessed all from the homepage at localhost:80, here are the descriptions and links for direct access to the main containers (not all of them!):
- Homepage can be accessed at localhost:80, and from there all the important information and containers can be accessed
- Visualisations are rendered and listed with Meteor at localhost:3000
- Data analyses processes are scheduled and maneged with Airflow at localhost:8080
- Data analyses processes can be written online with Cloud9 at localhost:8181
- Data analyses processes using Celery can also be monitored with Flower at localhost:5555
- Data stored in the Mongo database can be accessed with Nosqlclient at localhost:3300
- Data stored in the PostgreSQL database can be accessed with pgAdmin at localhost:5050
- All the containers can be managed with Portainer at localhost:9000
The homepage contains update instructions about how to use the platform and how to extend it with custom data analyses and visualisations. Please check the documentation of each container from their developers.
