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.

'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.

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.

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

This repository contains a comprehensive end-to-end Azure Data Engineering solution, covering the entire data lifecycle from ingestion to reporting. The project utilizes various Azure services and tools to achieve efficient data handling, transformation, and reporting.

Tracking Tweet sentiment at scale using a pretrained Huggingface transformer (classifier) Model

A comprehensive guide to 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

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.

This repository demonstrates the creation of a robust, 3-tier data lake using AWS resources.

In this project, we setup and end to end data engineering using Apache Spark, Azure Databricks, Data Build Tool (DBT) using Azure as our cloud provider.

This project builds a cloud-based pipeline to extract NYC taxi data from an API and store it in Azure Data Lake Storage (ADLS). Databricks and PySpark are used to transform the data through the medallion architecture (Bronze → Silver → Gold). Delta Lake ensures reliable storage, and Power BI provides visual insights for data-driven decision-making.

End-to-End Data Pipeline for NYC Green Taxi trip data using Azure Data Factory, Data Lake, and Databricks following the Medallion architecture

Scalable DBT project with Medallion Architecture & SQL Server. Unlocking insights for smarter urban planning.

Building a Data Lakehouse using the Medallion architecture.

Data Warehouse with ELT pipelines