A comprehensive guide to building a modern data warehouse with SQL Server, including ETL processes, data modeling, and analytics.

Arcane Insight is a data analytics project designed to harness the power of SQLMesh & DuckDB to collect, transform, and analyze data from Blizzard’s Hearthstone API. Focused on card statistics and attributes, this project reveals detailed insights into card mechanics, strengths, and trends to support BI and strategic analysis.

Building a modern data warehouse with SQL server, including ETL processes, data modeling, and analytics.

A production-ready PySpark project template with medallion architecture, Python packaging, unit tests, integration tests, CI/CD automation, Databricks Asset Bundles, and DQX data quality framework.

'Talk to Your Factory' demo leveraging Edge (Azure IoT Operations), Cloud (Microsoft Fabric), and a Factory Agent (Azure OpenAI), to streamline factory operations. It allows real-time, natural language communication with factory systems, helping operators quickly identify issues, boost efficiency, and minimize downtime.

This project implements a Lakehouse Medallion Architecture using modern Data Stack tools such as Fivetran, Snowflake and dbt. The ficticious organization is an e-commerce company.

Revolutionary AI-powered ETL pipeline combining Medallion Architecture with autonomous agents on Databricks. Built during Accenture internship using LangChain/LangGraph for self-healing data transformations. Zero-touch Bronze→Silver→Gold processing with complete LangSmith observability.

A cloud-native data pipeline and visualization project analyzing Formula 1 racing data using Azure, Databricks, Delta Lake, Tableau, and Python for insightful EDA and interactive dashboards.

This repo provides a step-by-step approach to building a modern data warehouse using PostgreSQL. It covers the ETL (Extract, Transform, Load) process, data modeling, exploratory data analysis (EDA), and advanced data analysis techniques.

Extract data from many databases of Labor, Invalids and Social Affairs sectors and convert to appropriate structure and format, then upload to shared data warehouse and data mart. Thanks to that, people of state agencies can easily retrieve and analyze data based on the compiled data warehouse.

End-to-end data engineering pipeline with real-time streaming, cloud processing, and analytics. Built with Apache Kafka, Spark, AWS Glue, and Snowflake using Apache Iceberg tables.

The Olympic Dataset Analysis project involves exploring and analyzing historical Olympic Games data, including athletes, countries, medals, sports, and event performance. Using data analytics and visualization techniques, this project uncovers patterns in Olympic history, top-performing countries, and athlete achievements.

End-to-end Azure Data Engineering project using Medallion Architecture, Databricks, Synapse, and Power BI.

This project involved the development and implementation of a Data Lake architecture to support an AI model capable of generating image captions. The architecture was designed to efficiently ingest, process, and centralized store large volumes of image and text data.

Implemented enhanced analytics for trip transactions and ride-based data from SQL Server using Medallion Architecture with Azure Data Factory and Databricks.

Building a modern warehouse with SQL Server , including ETL processes , data modeling and analytics.

Building a modern data warehouse with SQL Server, including ETL processes, data modeling, and analytics

In addition to exploring good practices in Python, the purpose is to apply concepts from Medallion Architecture, object-oriented programming and ETL, using public social data from Brazilian municipalities. This repository also uses DuckDB and Streamlit to display results

University of Derby 2024-UDOL-2025-05-26-6CC552

spark, databricks, kafka, batch and stream-processing