A data engineering project with Airflow, dbt, Terrafrom, GCP and much more!

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Real time data engineering project

This repository contains my work from the Data Mining Tools course, focusing on practical applications of data mining techniques. It includes various projects involving data preprocessing, feature engineering, and the implementation of machine learning algorithms. One key project explores the analysis of the All Steam Games and Metadata dataset

An Online Data Analytics Platform and Research Management System for Science, Technology, Engineering, Agriculture, Fisheries and Mathematics (STEAM) Education in the Philippines.

A comprehensive data engineering project that scrapes product data from multiple e-commerce platforms (MakeUp, BooksToScrape, and Steam games), processes it, stores it in HDFS, and integrates it into a MySQL database for advanced business analytics.

A browser-based Rankine Cycle simulator for mechanical engineering students. Built with Streamlit and powered by CoolProp for real steam property data. Simulates turbine & pump work, heat input, and thermal efficiency.

This project analyzes historical video game pricing data to predict the best time to purchase games on Steam. It uses a synthetic but realistic dataset of sales events for 20 popular games. The application performs data cleaning, feature engineering, machine learning modeling, and dashboard visualization to support consumer decision-making.

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An ETL pipeline project that ingests, cleans, and structures Steam public app data using Python.

data engineering project to pull steam data

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Data engineering on socket steaming platform

Data Engineering Program - Kafka Steaming in Python/Faust

Data engineering pipeline that ingests Steam API data into a Delta Lake data lake.

End-to-end data engineering, analytics, and data science project using Steam game data on Databricks.

Capstone project for Data Engineering Zoomcamp - released Video Games on Steam and their languages

An end-to-end data engineering pipeline that ingests, processes, and visualizes data from Steam Games Dataset

A personal data engineering project that extracts application/game data from steam webstore API and steamspy API

This is my project submission for the Data Engineering Zoomcamp 2026, building a pipeline for Steam games data.

A reproducible end-to-end data engineering pipeline for steam game information, focusing on reviews

Exploratory Data Analysis for the top 1500 games on Steam. Data Engineering practice using Docker, PostgreSQL, Airflow, Kibana, and ElasticSearch

Machine learning project classifying hidden gem games on Steam using Python and scikit-learn. Covers data cleaning, feature engineering, classification modelling and data leakage detection.

End-to-end data engineering project that extracts data from Steam APIs, processes it using Medallion Architecture in Databricks, and visualizes insights through Power BI dashboards.

A data engineering pipeline that collects Steam game stats (player counts, prices, discounts) and transforms them into actionable insights using a Bronze-Silver-Gold architecture.

🚀 Steam Games Success Analysis - Big Data Project 🎮 Analyzing game success on Steam using PySpark, Hadoop, MapReduce, and MongoDB. Key tasks include data cleaning, feature engineering, EDA, machine learning, and pricing strategy analysis. Predicting game pricing and success using classification models. 📊🚀

An end-to-end data engineering project that captures live Steam reviews using Python, processes them in real-time with Apache Spark, and visualizes analytics on a Metabase dashboard.

Part 1 of the Building Energy Efficiency project: data preparation, feature engineering, EDA, and predictive modeling of electricity, chilled water, steam, and hot water. Includes LightGBM models with interpretability (SHAP) and error analysis.

A comprehensive machine learning project for finding and recommending similar games on Steam based on their characteristics, tags, genres, and user reviews. This project demonstrates advanced data visualization, feature engineering, and recommendation system implementation.

This repo contains the core data engineering pipeline for Steam analytics, covering the Brown (raw ingestion) and Silver (cleaned/standardized) stages. It handles hourly API extraction, data validation, type correction, KPI normalization, and prepares high-quality structured data that feeds directly into the analytics dashboards in the Gold layer.