Template for fullstack webapp React, FastAPI (alternative Flask), Elastic Search, RabbitMQ, Celery setup with Docker-Compose
Budget: €30 – €250 EUR
## Project description:
We need a working fullstack template for a webapp based on React, FastAPI (alternative Flask), Elastic Search stack as a persistent datastore (including Kibana).
The project should be deployed via Docker-Compose.
All project results will be stored and managed in a GIT repository.
The major result of this project is to bring all the puzzle pieces together, so that we have a good basis to proceed with further development.
## Main Features of the application:
1. The frontend provides basic usability for managing a datagrid additional there is an import feature to initially import a CSV dataset into the database, also there is a login mask based on OAuth2
2. The import feature is calling a microservice that in the simplest way takes a csv and updates the existing table in the database
3. Implementation of an example Trigger based on a certain cell value in the dataset, that when triggered routes a message (JSON) to a Celery worker via the message broker (RabitMQ) and sends back a result
4. Implementation of an example Trigger based on an delete event in the dataset, that when triggered routes a message (JSON) to a Celery worker via the message broker (RabitMQ) and sends back a result
5. Kibana UI is accessible
We need a working fullstack template for a webapp based on React, FastAPI (alternative Flask), Elastic Search stack as a persistent datastore (including Kibana).
The project should be deployed via Docker-Compose.
All project results will be stored and managed in a GIT repository.
The major result of this project is to bring all the puzzle pieces together, so that we have a good basis to proceed with further development.
## Main Features of the application:
1. The frontend provides basic usability for managing a datagrid additional there is an import feature to initially import a CSV dataset into the database, also there is a login mask based on OAuth2
2. The import feature is calling a microservice that in the simplest way takes a csv and updates the existing table in the database
3. Implementation of an example Trigger based on a certain cell value in the dataset, that when triggered routes a message (JSON) to a Celery worker via the message broker (RabitMQ) and sends back a result
4. Implementation of an example Trigger based on an delete event in the dataset, that when triggered routes a message (JSON) to a Celery worker via the message broker (RabitMQ) and sends back a result
5. Kibana UI is accessible