This project was generated with Angular CLI version 15.2.1.
Please use package.json / package-lock.json to install all third party libraries needed, before running the Angular server.
Run ng serve for a dev server. Navigate to http://localhost:4200/. The application will automatically reload if you change any of the source files.
Run ng build to build the project. The build artifacts will be stored in the dist/ directory.
To get more help on the Angular CLI use ng help or go check out the Angular CLI Overview and Command Reference page.
docker-compose exec db psql -U myuser -c "COPY mytable(name, age, gender) FROM '/data/data.csv' DELIMITER ',' CSV HEADER;" docker-compose up -d
The database needs to be running and filled with data, so that the backend can server that data to the frontend.
To initiate the database of the project please follow the instruction found inside the db_local_config.md file under the db sub-directory of the server directory:
/server/db/db_local_config.md
To install all of the required packages for the backend python scripts open a terminal and navigate to the server directory of this project:
.../server> pip install -r requirements.txt
To initiate the backend server (API) part of the application, execute (directly from the main directory of the project):
...> py -m uvicorn server.main:app --host 0.0.0.0 --port 8000 --reload
Please note that for any of the following scripts to work, the database's docker container needs to be up and running!
Also, please note that all scripts are tailored to work with the database configuration described in the db_local_config.md file (see the corresponding section), so make sure to apply the necessary changes to the python scripts if your environment is setup is different.
To download data from diavgeia and load the staging environment table you have to run the fetch.py python script found within the data_prep sub-directory of the server directory. You can alter lines 13 and 14 to configure the start and end years respectively. Be aware that if end_year is earlier than start_year, no data will be downloaded.
.../server/data_prep> py fetch.py
To execute the ETL pipeline for this project, execute the etl.py python script found within the ETL sub-directory of the server directory.
ETL can be run simulatenously with fetch.py, although it is suggested to first wait for the fetching process to finish downloading all of the available data.
.../server/ETL> py etl.py