end-to-end Data Engineering Pipeline project

end-to-end Data Engineering Pipeline project

Over the past days, I worked on building a small but practical Data Engineering project using Python to process and analyze a hotel booking dataset.

The goal of the project was to design a structured data pipeline that transforms raw data into meaningful analytical insights.

المشروع ده ساعدني أطبق بشكل عملي concepts مهمة في Data Engineering بدل ما تكون مجرد theoretical knowledge.

The pipeline follows the Medallion Architecture, which is widely used in modern data platforms:

Raw Data → Bronze → Silver → Gold

Here is the workflow I implemented:

1️⃣ Data Ingestion (Bronze Layer)

The pipeline starts by loading the raw dataset using Pandas and storing it as the Bronze layer, which keeps the original data unchanged for reliability and traceability.

2️⃣ Data Exploration

Before applying any transformations, I performed exploratory analysis to understand the dataset and identify potential data quality issues such as:

• Missing values

• Duplicate records

• Invalid or extreme values

• Data type inconsistencies

الخطوة دي كانت مهمة جدًا عشان أفهم الداتا كويس قبل ما أبدأ التنظيف.

3️⃣ Data Cleaning & Transformation (Silver Layer)

In this stage, I applied multiple data quality checks including:

• Removing duplicates

• Handling missing values

• Dropping columns with excessive missing data based on business logic

• Removing invalid price values

• Detecting and filtering outliers

• Validating guest-related information

I also applied feature engineering techniques to enhance the dataset for analysis.

After transformation, the dataset was stored as the Silver layer.

4️⃣ Data Analytics (Gold Layer)

From the cleaned dataset, I generated several analytical metrics including:

• Cancellation Rate

• Average Price per Month

• Top Countries by Bookings

• Hotel Type Distribution

• Average Stay Duration

These results were stored as Gold analytical tables, which represent the final layer used for analysis or dashboards.

5️⃣ Pipeline Automation

To make the workflow reproducible, I connected all steps into a single pipeline script so the entire process can be executed with one command:

python main.py

So the pipeline flow becomes:

Raw Data → Bronze → Silver → Gold

Some insights discovered from the dataset:

• Cancellation rate ≈ 27.6%

• City hotels receive more bookings than resort hotels

• Prices tend to increase during summer months

• Average stay duration ≈ 3.6 nights

Through this project I practiced:

✔ Data Cleaning

✔ Data Quality Validation

✔ Feature Engineering

✔ Data Pipeline Design

✔ Medallion Architecture

Next step in this journey is to extend the project by adding:

• SQL Data Warehouse integration

• Interactive dashboards using Streamlit