🎨 UI for the Free Data Engineering Zoomcamp Course provided by DataTalksClub

Data Engineering examples for Airflow, Prefect, and Mage.ai; dbt for BigQuery, Redshift, ClickHouse, PostgreSQL; Spark/PySpark for Batch processing; and Kafka for Stream processing

An ETL data pipeline that extracts data from source and loads it to destination, automated using mage.ai

pokéFastApi: A backend API with Docker Compose integration for retrieving and searching Pokémon data using FastAPI and DuckDB.

Using machine learning, feature engineering, and web scraping, I created an end-to-end laptop price prediction website by scraping data from a popular Iranian source. Empowering users with accurate pricing estimates and model comparisons

ETL pipeline on the New York Times data with apache superset for data visualization

An ETL Pipeline built over GCP and orchestrated by Mage, which involves Extracting Data from GCS Bucket, building Dimensional Model, loading the Data into BigQuery and a Looker Dashboard for further analysis.

Data Engineering Zoomcamp Final Project

Datatalks Club Free Data Engineering Zoomcamp Project

Using Mage.ai orchestration tool for ETL process

🚕 Performing Data Analytics on NYC Taxi data using GCP and MageAI

Engineered a real-time Cryptocurrency dashboard on AWS using Python, MageAI, SQL, and NLP for sentiment analysis, with ML forecasting and PowerBI integration for dynamic reporting and decision-making.

A covid 19 Data engieneering pipeline

Data Engineering Swiss Air Quality

DataTalks Club - Machine Learning Operations Zoomcamp (Cohort 2024)

Data engineering project for the DEZoomcamp 2024

Real-time data pipeline for collecting, processing, and visualizing telemetry data from Assetto Corsa Competizione using Mage, Kafka, PostgreSQL, and Metabase for interactive insights into vehicle performance and driver statistics.

DE Zoomcamp 2024 Final Project 🧙

This project is a Data Engineering solution implemented to validate the data types of columns in PostgreSQL tables in a DataMart. It aims to validate whether the data stored in tables conforms to the expected data types, improving data integrity and reliability.