DataWharf is a Database Designer/Modeler/Analyzer Tool web application.

View ChangeLog.md for list of enhancements and fixes.

View Data Architecture and Modeling with DataWharf Article for User and Developer documentation

View Data Architecture and Modeling with DataWharf Presentation Deck (Slides)

• Data Architecture and Modeling with DataWharf
• Tutorial - Installing DataWharf Locally

https://www.youtube.com/@EricLendvai

Sample screen of Data Dictionary Visualization

For Windows and Mac users, the easiest is to Install Docker Desktop.
For Windows users you can use the following article to learn how to setup WSL, Docker Desktop
If you don't already have access to a PostgreSQL server, install version 14 or above on your local machine.
Create an empty database "DataWharfDemo" for example and update the file "config_demo.txt" with PostgreSQL connection and login information.
There are 3 different ways to build a docker container. The slowest, but most up to date method is to use the docker file "Dockerfile_Demo_Complete_Ubuntu_Latest".
"Dockerfile_Demo_Complete_Ubuntu_Latest_With_Builder" will create a smaller image.
The fastest method to build a docker image is to use "Dockerfile_Demo_Using_DockerHub_Ubuntu_22_04". DataWharf will use less than 30 Mb of ram at first.
The current builds are using Ubuntu 22.04.
The following commands can be used to create a docker image and start it, assigning port 8080.

docker build . -f Dockerfile_Demo_Using_DockerHub_Ubuntu_22_04 -t datawharf_demo_using_dockerhub_baseimage:latest
docker run -d -p 8080:80 datawharf_demo_using_dockerhub_baseimage:latest

Optionally you could add "--no-cache" to force complete rebuilds.

Open a browser to "http://localhost:8080"
The initial login ID is "main" and the password is "password".
Once you logged in, to see DataWharf's own data dictionary use the following steps:

1. Go to "Settings" and add an "Application", "DataWharf".
2. Go to "Data Dictionary", select "DataWharf", use the "Import" option, and from the repo use the latest ExportDataDictionary_DataWharf_*.zip
   You can do the same for "Projects" and "Models".

Review the following Instructions to install DataWharf using docker
This method will require access to https://hub.docker.com/ since it will download the latest build version of DataWharf.

The following is the list of additional open source projects used to design, build and deploy DataWharf:

DataWharf can run on Windows, Linux or any platforms supported by the above list of repos/products.

View Todo.md for list of upcoming fixes and enhancements.

In order to develop in any environement you can use the VS Code devcontainer provided in this repo. Install remote containers extension: https://aka.ms/vscode-remote/download/containers For Windows users you can use the following article to learn how to setup WSL, Docker Desktop

Source: here

Install Lima and Docker-CLI:

brew install lima docker

Create Linux VM with Dockerd:

curl https://raw.githubusercontent.com/lima-vm/lima/master/examples/docker.yaml -O
limactl start ./docker.yaml
limactl shell docker
sudo systemctl enable ssh.service

There is one important tweak in the Lima configuration. It’s necessary to enable write operation otherwise, the workspace mounted from VS Code is read-only. Open file ~/.lima/docker/lima.yaml and add writable flag to desired folder:

mounts:
- location: "~"
  writable: true

Restart Lima to apply changes.

limactl stop docker
limactl start docker

Create context for Docker-CLI to connect to dockerd running in the VM:

docker context create lima --docker "host=unix://${HOME}/.lima/docker/sock/docker.sock"
docker context use lima

• Reopen the folder in the dev container: press F1 and then do >Remote-Containers: Open Folder in Container...
• You can now use the following tasks defined by VS Code to compile/debug:
  • <Compile Debug>: Compiles with debug settings and deployes the executable inside the backend part of the apache website: /var/www/Harbour_websites/fcgi_DataWharf/backend/. (Note: only the exe will be copied there for now, changes done to website parts such as .js or .css files need to be copied there manually).
  • <Compile Release>: Build without debug settings.
  • <Debug>: Attaches to the running executable inside Apache.
• Go to Ports view and open the port that exposes port 80 on the host (e.g., http://localhost:60677)

• The PostgreSQL DB is also accessible from the host via the exposed port 5432.
• Using e.g., PGAdmin you can connect using the following credentials:
  • Host: localhost
  • Port: 5432
  • Username: datawharf
  • Password: mypassord