Here's how to get DataQuest's Data Engineering Track missions' content to work on your localhost. Using data from my Valenbisi ARIMA modeling project, I document my steps using PostgreSQL, Postico, and the Command Line to get our DataQuest exercises running out of a Jupyter Notebook.

PPP loan data processing pipeline: cleans, encodes, and geo-maps loan data into state-level databases for analysis and visualization.

Spotify Data Engineering Project

This repo serves as a central hub for visualizing and understanding the intricate architectures and orchestrated workflows that power modern data operations and analytics solutions. Whether you're a data engineer, analyst, or architect, you'll find valuable resources to support your design and implementation efforts.

Este repositorio contiene los notebooks utilizados en la asignatura Ciencia de Datos de la carrera de Ingeniería en Informática. Los notebooks cubren una variedad de temas relacionados con el análisis de datos, minería de datos, machine learning, y el uso de herramientas como Scikit-learn, pandas, numpy, y más.

Production-ready data pipeline with Jupyter notebook, SQLite database modeling (star schema), and automated ETL workflows for customer churn analysis and segmentation.

Cloud ETL pipeline for LendingClub 2018Q4 loan data using Azure Databricks (Spark), ADLS Gen2, and Azure SQL. Includes notebooks, PySpark modules, and SQL scripts.

An extension enabling the monitoring of Apache Airflow DAGs directly from Jupyter notebooks. Tailored for developers and data scientists, it simplifies tracking specific DAGs, reduces unnecessary friction, and allows severity levels setup for failed DAGs.

A learning exercise demonstrating a manual data pipeline using a Jupyter Notebook, extracting car data from an AWS S3 datalake, performing transformations with Pandas, and loading the processed data into a Dockerized PostgreSQL database.

📈 A modular, multi-notebook visualization suite built with Plotly and Streamlit, showcasing advanced charting techniques, subplot dashboards, z-score bands, regression overlays, and app-based exploration. Ideal for mastering interactive data storytelling and production-ready Python workflows.

This project was made in Jupyter Notebook, it included data ingestion from a CSV file, PDF file, and web scraping. Then transforming and cleaning the data, and lastly storing the data in a storage solution, in which I used MongoDB to store the data.

📊 A comprehensive pandas mastery project with 10 modular Jupyter notebooks covering data loading, cleaning, grouping, merging, time series, visualization, and performance profiling. Includes real-world workflows, Docker, Streamlit, and reusable utils. Ideal for data scientists and analysts to learn, practice, and refer. Practice-ready and modular.

An ELT data pipeline 🚚 for analyzing Brazilian e-commerce data. Processes CSVs and public API data using Python and Pandas, loads it into an SQLite database, and analyzes revenue/delivery metrics with SQL. Visualizations and the final report are created in a Jupyter Notebook.