
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 .
Please keep an eye on the billing cost so that we don't overrun into huge billing costs.
Build a realistic AWS data engineering project that ingests healthcare data from Amazon RDS PostgreSQL into an Amazon S3 data lake using AWS Glue.
You will work through the project as a data engineer—from understanding the business context and source systems to defining the pipeline contract, making design decisions, setting up AWS, implementing the solution, validating the output, and scheduling repeatable runs.
In this project, you will:
Prepare the source: Set up a PostgreSQL database in Amazon RDS
Build the Glue job: Ingest multiple source tables using AWS Glue and PySpark
Load incrementally: Use watermarks to process new and updated records
Store the data: Write the output to Amazon S3 as Parquet files
Track each run: Add ingestion metadata for auditing and validation
Catalog the data: Use AWS Glue Crawlers and the Glue Data Catalog
Validate the output: Query the ingested data using Amazon Athena
Schedule the pipeline: Use Glue Workflows and Triggers for repeatable runs
More than isolated service demonstrations
You will build the project inside your own AWS account using the provided source code, healthcare sample data, configuration files, and infrastructure setup scripts.
The focus is not only on getting the pipeline to run. You will understand why each AWS 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 AWS data engineering project that you can practice, adapt for your portfolio, and explain clearly during interviews.
This course contains a promotion.