star-schema

The world's fastest open query engine for sub-second analytics both on and off the data lakehouse. With the flexibility to support nearly any scenario, StarRocks provides best-in-class performance for multi-dimensional analytics, real-time analytics, and ad-hoc queries. A Linux Foundation project.

Personal Data Engineering Projects

Udacity Data Engineering Nano Degree (DEND)

A framework for rapid reporting API development; with out of the box support for high cardinality dimension lookups with druid.

Production grade Supply Chain & Logistics Optimization platform handling 100,000+ records. Built with Star Schema, Advanced DAX, and AI Root Cause Analytics in Power BI.

Experiments with GitHub event data

Python scripts to set up a multidimensional model and run an ETL process for Ethereum data analysis.

A project to design a fact and dimension star schema for optimizing queries on a flight booking database using PostgreSQL, a relational database management system. This schema is well-suited for a flight booking database, as it allows for efficient querying of data such as booking dates, flight routes, and passenger information.

This repository is a place for the Data Warehousing course at the Information Systems & Analytics department, Santa Clara University.

End-to-end ELT batch pipeline | Medallion Architecture (Bronze → Silver → Gold) | dbt + Snowflake + AWS S3 + Apache Airflow + Docker | 3.5M+ records

Enterprise-grade Data Platform for NYC Taxi Analytics. Orchestrated with Airflow (Astro) & dbt, served via FastAPI & Power BI. Features Medallion Architecture, Data Quality Observability (Slack), and Star Schema modeling.

Building a Modern Data Warehouse using SQL Server, Including ETL processes, Data modeling, Data analytics.

End-to-end E-Commerce Lakehouse built on Databricks using Medallion Architecture (Bronze → Silver → Gold). Ingests raw CRM and ERP data, applies PySpark transformations, and delivers a star schema via Spark SQL stored as Delta Tables in Unity Catalog.

This project encompasses the complete data lifecycle, from data extraction and transformation to in-depth analysis and compelling visualizations. The process is divided into three main phases

Batch & streaming data pipelines built using Databricks with Pyspark and modeled the data into star schema to analyze in PowerBI, Formula-1 racing data from multiple data sources, APIs.

Create Data Lake on AWS S3 to store dimensional tables after processing data using Spark on AWS EMR cluster

Demonstrating the core skills acquired by making use of Advanced Database Modeling.

A complete end-to-end data warehouse implementation — from raw data ingestion to actionable business insights. Also followed industry best practices in Data Engineering, ETL, Data-Cleansing, Data Modeling, and Analytics.

Bases de Datos - 2025 - UTN