Data Warehouse & Business Intelligence Project

A end-to-end Data Warehousing & Business Intelligence (DWBI) project, built as a group assignment (Group 9). The project covers dimensional modeling, OLAP/MOLAP analysis, query performance tuning, DAX-based calculations, and an interactive Power BI dashboard.

📌 Overview

This repository documents the full lifecycle of building a data warehouse — starting with an ETL (Extract, Transform, Load) pipeline to move and prepare data, followed by schema design and justification, OLAP/MOLAP implementation, performance optimization (indexing, partitioning, join comparisons), and final reporting and dashboarding in Power BI.

🗂️ Repository Contents

File	Description
23l-2551_Lab11.pbix	Power BI project file with the interactive dashboard/report
Compiled_code_for_Deliverable_1.sql	SQL script covering the first project deliverable
Group#9_Schema_Justification.sql	Star/snowflake schema design and justification for the warehouse
Group#9_OLAP.sql	OLAP queries used for multidimensional analysis
Group#9_MOLAP.sql	MOLAP implementation queries
Group9_Indexing_Partitioning.sql	Indexing and partitioning strategies for query optimization
Group9_JoinPerformance.sql	Comparison of join strategies and their performance impact
Group9_DAX.txt / Group9_AdvancedDAX.txt	Basic and advanced DAX measures used in Power BI
Basic DAX.docx	Documentation of DAX formulas and use cases
Joins Comparison.docx	Written comparison/analysis of join types
Group9_DAX_&_Advanced_DAX_png.pdf	Screenshots of DAX outputs
Group9_dashbaord_ss.pdf	Dashboard screenshots
Model_View_Group9.png	Data model / schema diagram
Group9_report_dw.pdf	Data warehouse project report
Group9_FinalReport.pdf	Final consolidated project report
DWBI_Deliverable4_Group9.zip	Archive of the final deliverable submission

🔄 ETL Pipeline

The core of this project is the ETL (Extract, Transform, Load) pipeline that feeds the data warehouse:

• Extract – Raw source data is pulled from the operational/staging tables into the pipeline.
• Transform – Data is cleaned, standardized, and restructured to fit the dimensional (star/snowflake) schema — including handling of keys, deduplication, and business-rule transformations documented in Group#9_Schema_Justification.sql.
• Load – Transformed data is loaded into the fact and dimension tables of the warehouse, ready for OLAP/MOLAP analysis and reporting.
• The Compiled_code_for_Deliverable_1.sql file contains the compiled ETL/schema-building scripts for the first deliverable stage of the pipeline.
• The pipeline output directly powers the OLAP cubes, DAX measures, and the final Power BI dashboard.

🧱 Key Concepts Covered

• ETL Pipeline – Extracting, transforming, and loading data into the warehouse
• Dimensional Modeling – Star/snowflake schema design with justification
• OLAP & MOLAP – Multidimensional data analysis and aggregation
• Query Optimization – Indexing, partitioning, and join performance comparisons
• DAX (Data Analysis Expressions) – Basic and advanced measures for Power BI
• Business Intelligence Dashboard – Interactive Power BI report (.pbix)
• Documentation & Reporting – Full project report and schema visuals

🛠️ Tools & Technologies

• SQL (schema design, OLAP/MOLAP, performance tuning)
• Power BI (data modeling, DAX, dashboarding)
• Microsoft Word / PDF (project documentation and reporting)

🚀 How to Use

1. Open 23l-2551_Lab11.pbix in Power BI Desktop to explore the interactive dashboard.
2. Review the .sql files in any SQL client to see schema design, OLAP/MOLAP logic, and optimization scripts.
3. Refer to Group9_FinalReport.pdf for the complete write-up of methodology, design decisions, and results.