
STEP 1: Install VS Code
Search: VS Code download
Download and install
During install:
Add to PATH
Add "Open with Code" options
Open VS Code once to verify
STEP 1: Install WSL
Open PowerShell as Administrator
Run:
wsl --install -d Ubuntu-22.04
Restart system if prompted
STEP 2: Setup Ubuntu
Open Ubuntu from Start menu
Create username: unix
Create password: unix
NOTE:
Password will NOT be visible while typing
STEP 3: Update System
Run:
sudo apt update
sudo apt upgrade -y
STEP 4: Install Python
Run:
sudo apt install python3 python3-pip -y
STEP 5: Verify Python Installation
Run:
python3 --version
STEP 6: Verify Python Location
Run:
which python3
Expected:
Python should be located at:
/usr/bin/python3
STEP 1: Install Extensions in VS Code
Open VS Code
Install:
Python (Microsoft)
WSL (Microsoft)
STEP 2: Open Project in WSL
Open Ubuntu terminal
Run:
cd ~
mkdir -p sample
cd sample
To open VS Code in WSL:
code .
STEP 3: Verify WSL Connection
Terminal should look like:
unix@machine:~/sample$
STEP 4: Set Default Terminal (if needed)
In VS Code:
Ctrl + Shift + P
Search:
Terminal: Select Default Profile
Choose:
WSL / Ubuntu
STEP 5: Verify Python
Run:
python3 --version
which python3
STEP 1: Prepare Working Directory
Open Ubuntu
Run:
cd ~
mkdir -p data0to1_pipelines
cd data0to1_pipelines
mkdir azure
cd azure
STEP 2: Copy Project Code
If your zip file is in Windows Downloads:
Unzip it
Copy the contents into the above WSL path using File Explorer
Then run:
code .
This will open VS Code from the azure (or respective cloud) folder.
STEP 3: Give Execute Permission
Run:
find . -type f -name "*.sh" -exec chmod 755 {} \;
STEP 4: Install Required Utilities
Run:
sudo apt install unzip dos2unix -y
STEP 5: Convert Shell Script Format
Run:
find . -type f -name "*.sh" -exec dos2unix {} \;
STEP 6: Going Forward – Open Project in VS Code
Run:
cd ~/data0to1_pipelines//
code .
Build a realistic Azure data engineering project that ingests financial services data from MySQL into Azure Data Lake Storage Gen2 using Azure Data Factory.
You will work through the project as a data engineer—from understanding the business context and source systems to defining the pipeline contract, designing incremental loads, setting up Azure resources, implementing the solution, validating the output, and scheduling repeatable runs.
In this project, you will:
Prepare the source: Set up multiple financial services tables in MySQL.
Connect the source: Configure a self-hosted integration runtime and ADF linked services.
Build the pipeline: Ingest multiple source tables using Azure Data Factory.
Load data incrementally: Use watermarks to process new and updated records.
Store the data: Write the raw data to Azure Data Lake Storage Gen2.
Track each run: Maintain configuration, run history, and watermarks in Azure SQL.
Validate the output: Query and verify the ingested data using Azure Synapse.
Schedule the solution: Use ADF triggers to run the pipeline on a schedule.
You will build the project in your own Azure account using the provided source code, sample MySQL data, configuration files, and infrastructure setup scripts.
The focus is not only on getting the pipeline to run. You will understand why each Azure service is used, how the components work together, and how the pipeline handles new and updated source records during subsequent runs.
By the end of the course, you will have a complete Azure data engineering project that you can practice, adapt for your portfolio, and explain clearly in interviews.
This course contains a promotion.