A Data Warehouse project consists of several parts:

• Data collection and settle down, which refers to OLTP (Online Transaction Processing)
• Data transformation and integration, which refers to ETL (Extract, Transform, Load)
• Data Warehouse design and implementation, which refers to OLAP (Online Analytical Processing)
  • The design of the data warehouse normally happens before the ETL process
• Data analysis and visualization, which refers to BI (Business Intelligence)
  • This is the end goal of the data warehouse project, which is to provide insights to the business, answer business questions, and help the business make decisions

Let's give the AdventureWorks database as an example to illustrate the above concepts.

Adventure Works Cycles is a fictitious, multinational manufacturing company that sells bicycles and accessories.

git clone https://github.com/PascalSun/DW_2024.git

If you are not familiar with git, please learn the basic concepts about git and GitHub.

It is a must-have skill for a software engineer.

Here is a good tutorial for beginners: https://www.w3schools.com/git/

And here is official documentation: https://git-scm.com/book/en/v2/Getting-Started-Installing-Git

Also, if you do not have a code editor, please install the VSCODE: https://code.visualstudio.com/download

Make sure you have docker and docker-compose installed on your machine.

Then run docker compose up -d to start the databases and jupyter lab environment.

Try to understand how many services inside the docker compose file, and what they are used for.

Which part of the data warehouse project do they belong to?

If you are not familiar with docker, please learn the basic concepts about docker.

We just put down a tutorial for you to get started with docker: https://uwa-nlp-tlp.gitbook.io/it-support/docker-101

To confirm all the four containers are running, you should be able to run

docker ps 

And it will show up four containers: pgdb, pgadmin, sqlserver and jupyterlab.

If you do not have this four, check which one is missing, and try to check the logs of that container via command

docker logs -f <container_name>

to see what is wrong.

If you run the docker compose up command within the powershell or terminal provided by Windows.

Due to the end of file conflict between Unix based and Windows-based systems, sql server may not start properly.

So in this suitutation, you will find you miss the sqlserver container when you run docker ps.

To resolve this issue, you need to open the whole project with VSCode, and go to the file OLTP/sqlserver/install.sh, click right bottom-corner, and change the line ending from CRLF to LF, and then save it.

You need to do the same for file OLTP/sqlserver/startup.sh.

Then run the

docker compose up --build -d

command again to rebuild the image and recreate the containers.

For users with ARM architecture, you may encounter the issue that the sqlserver and jupyterlab container cannot start properly. This is because the mcr.microsoft.com/mssql/server image does not support ARM architecture. To resolve this issue, you need to change the setting in docker, enable the Use Rosetta for x86_64/amd64 emulation on Apple Silicon option.

Open setting => General tab in Docker Desktop, and check the option Use Rosetta for x86_64/amd64 emulation on Apple Silicon. Click Apply & Restart to restart the docker desktop.

Clean the images, then run the docker compose up --build -d command again.

We already provide a container inside the docker-compose file which named jupyterlab for you to do the development, but if you want to run the code locally, you can follow the steps below. We encourage you to explore both options, and understand the pros and cons of each option. We will not assess it, but the interviewers may ask you about it.

1. Need to install graphviz and corresponding python package, this is to draw the ER diagram inside python.

   • brew install graphviz
   • pip install graphviz

2. Create a virtual environment with python>=3.9 via venv or conda

   •  python3 -m venv venv
      source venv/bin/activate

   •  conda create -n dw python=3.9
      conda activate dw

3. Install the requirements

   • pip install -r requirements.txt

4. Run Jupyter Lab under current project directory

   • jupyter lab