Data Warehouse and Analytics Project

Data Warehouse and Analytics Project

This project demonstrates a comprehensive data warehousing and analytics solution, from building a data warehouse to generating actionable insights. Designed as a portfolio project, it highlights industry best practices in data engineering and analytics.

?️ Data Architecture

The data architecture for this project follows Medallion Architecture Bronze, Silver, and Gold layers:

Bronze Layer: Stores raw data as-is from the source systems. Data is ingested from CSV Files into SQL Server Database.

Silver Layer: This layer includes data cleansing, standardization, and normalization processes to prepare data for analysis.

Gold Layer: Houses business-ready data modeled into a star schema required for reporting and analytics.

? Project Overview

This project involves:

Data Architecture: Designing a Modern Data Warehouse Using Medallion Architecture Bronze, Silver, and Gold layers.

ETL Pipelines: Extracting, transforming, and loading data from source systems into the warehouse.

Data Modeling: Developing fact and dimension tables optimized for analytical queries.

Analytics & Reporting: Creating SQL-based reports and dashboards for actionable insights.

? This repository is an excellent resource for professionals and students looking to showcase expertise in:

SQL Development

Data Architect

Data Engineering

ETL Pipeline Developer

Data Modeling

Data Analytics

?️ Important Links & Tools:

Datasets: Access to the project dataset (csv files).

SQL Server Express: Lightweight server for hosting your SQL database.

SQL Server Management Studio (SSMS): GUI for managing and interacting with databases.

Git Repository: Set up a GitHub account and repository to manage, version, and collaborate on your code efficiently.

DrawIO: Design data architecture, models, flows, and diagrams.

Notion: All-in-one tool for project management and organization.

Notion Project Steps: Access to All Project Phases and Tasks.

? Project Requirements

Building the Data Warehouse (Data Engineering)

Objective

Develop a modern data warehouse using SQL Server to consolidate sales data, enabling analytical reporting and informed decision-making.

Specifications

Data Sources: Import data from two source systems (ERP and CRM) provided as CSV files.

Data Quality: Cleanse and resolve data quality issues prior to analysis.

Integration: Combine both sources into a single, user-friendly data model designed for analytical queries.

Scope: Focus on the latest dataset only; historization of data is not required.

Documentation: Provide clear documentation of the data model to support both business stakeholders and analytics teams.

BI: Analytics & Reporting (Data Analysis)

Objective

Develop SQL-based analytics to deliver detailed insights into:

Customer Behavior

Product Performance

Sales Trends

These insights empower stakeholders with key business metrics, enabling strategic decision-making.