ETL process for creating a Data Warehouse using SQL Server (SSIS and SSMS). Project based on the AdventureWorks dataset.

Data Analyst Portfolio Project 1 - SQL and PowerBI - Cleaning and analysing the AdventureWorks dataset with SQL, then visualizing it with PowerBI

Example project using DBT, Databricks and AdventureWorks sample database

Repository for sample code for fun with Adventure Works Cycling. Code is based on Microsoft's AdventureWorks sample project.

The AdventureWorks_2010 project focuses on building staging tables as part of the ETL process to prepare and standardize raw business data for integration into a structured data warehouse environment.

ETL process using Pentaho Data Integration (Kettle), for Sales and Purchases Datamarts from Adventureworks, as the final project from the Data Management course from the Big Data & Analytics Masters @ EAE class of 2021

AdventureWorks is a fictitious multinational manufacturing company. The data set used for this report is from the AdventureWorks2014 version. The goal of this project is to demonstrate how building powerful and beautiful (I hope :)) reports in Power BI by following the data visualization best practices and the data modelling patterns, showing the awesome features of Power BI like Report Page Tooltip, Calculated Groups, Forecasting, What-If parameters, Time Intelligence Functions, complex DAX code, custom charts and how it is easy to integrate Power BI with Python/R script in order to build machine learning models and so creating advanced and predictive analytics reports. Reports Pages are as follow: - P&L Overview: Profit & Loss Report, in which I analyzed the economic status of the company from the point of view of Revenues and Expenditures. In this report page I compared the trend of actual amount and budget amount, unfortunately for 2011 year only (missing data for other years). You can notice the powerful feature of “report page tooltip” in this page, by hovering over the bar charts. - Internet Sales Overview: in this report page I analyzed the sales of products to customers via Internet. You can notice the forecasting feature of Power BI and the use of what-if parameters in order to build advanced analytics reports. Also I made use of calculated groups (for further info about visit https://www.sqlbi.com/articles/introducing-calculation-groups/). - Reseller Sales Overview: similar to the previous one, but this time the focus is on the sales of products to resellers. Things to notice in this report page are the comparison of current sales vs last year sales, the comparison of actual sales amount vs budget/quota sales amount, and the running total and moving/running average charts. - Product Inventory Overview: inventory management analysis of the company. I analyzed the current stock on hold vs the recent sales revenue, showing the value of current stock, the current units in stock and the stock ratio. - Products Overview: in this report page I analyzed the products by category, color, size, “ABC” class too. Two very interesting and powerful things to notice: the basket analysis using DAX and the basket/association rules analysis using R script and apriori machine learning model. I made use of the custom chart called “Network Chart” to represent the association rules between products. Through the basket analysis the user could know which product is likely to be bought with another one. - Customers Overview: customer analysis by age group, location, status, and so on. The customer could be classified as “Bike Buyer” and “Non Bike Buyer”. So I decided to build a machine learning model (in this case I used XGBoost algorithm in Python) in order to predict if a (new) customer would be a bike buyer or not. The model has a prediction accuracy of about 84% which it is not bad. - Employees Overview: employee analysis by sales location, title, age. Thing to notice is the ribbon chart through which I can analyze the performance of employees over time. - Reseller Overview: analysis of the performance of the resellers. Important dimensions to analyze are the business type, the product line, the reseller location.

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

Power BI project analyzing AdventureWorks' overall performance. Visualize data, track financials, and uncover insights."

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A Web API 2 project with WebApp, Core and Data components that make use of SimpleInjector, Mediatr and AutoMapper. Works with the AdventureWorks sample MSSQL database (http://msftdbprodsamples.codeplex.com/).

Data analysis project using the AdventureWorks sample dataset from Microsoft Learn. Includes exploratory data analysis, SQL queries, and Power BI visualizations to gain insights into sales, customers, and product performance.

This project involves analyzing AdventureWorks bike sales data to uncover key insights into sales performance by country, customer segments, and products. The findings informed strategies for targeted marketing, market expansion, promotional timing, and product quality improvements.

This project deals to transform raw data into professional-quality reports and dashboards to track KPIs, data modelling, compare regional performance, analyze product-level trends, and identify high-value customers for a fictional manufacturing company AdventureWorks Cycles using SQL, Power BI

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Dashboard of the AdventureWorks2022 in Power Bi

this project aims at performing ETL operations by extracting from flat files and loading into adventureworks data Warehouse

End-to-end Data Warehouse project using AdventureWorks database, implementing ETL with SQL Server, SSIS, SSAS, and Power BI.

This project demonstrates a comprehensive data engineering pipeline built on Azure. Using the AdventureWorks dataset as a source, the pipeline showcases the efficient flow of data from ingestion to transformation, storage, and visualization.

The project seeks to optimize the inventory management of FitCompany. This is done by analyzing the inventory, purchasing, production and sales data using the tools: Power BI and Microsoft SQL Server Management studio, from AdventureWorks 2019 database.

This project is a Sales Data Analysis Dashboard built using Power BI, leveraging data from the AdventureWorks 2022 Sales Data Mart. It provides key business insights, including total sales, customer segmentation, top-selling products. The dashboard was developed using DAX and Power Query to ensure accurate and efficient data visualization.

AdventureWorks Data warehouse project (SSIS)

Reporting Project using AdventureWorks and AdventureWorks2008R2

Azure Data Pipeline for AdventureWorks Project

A Visual Studio project for AdventureWorks database.

A sample DDD project based in Adventureworks database

A .NET project utilizing the AdventureWorks sample database

Power BI Dashboard project showing executive-level KPIs for AdventureWorks.

DBT project for AdventureWorks data warehouse: staging, marts, and transformation models