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Data Warehouse SQL End-to-End E.T.L.

This project presents a complete data warehouse solution, integrating CRM and ERP data into a structured, multi-layer (Bronze, Silver, Gold) ETL pipeline. Using SQL, we design and build data models including star schemas to support high-quality business reporting. Final datasets are visualized with Tableau dashboards, offering actionable insights and clear business value.

Project Features:

💾 Data architecture and layer design (Bronze, Silver, Gold). ⚙️ SQL-based ETL pipelines: data extraction, transformation, loading. 🔗 Integration of CRM and ERP data sources. 📊 Star schema and dimensional modeling. 📈 Tableau dashboard visualizations for business insights.

Technologies Used:

  1. Bronze Layer: Stores raw data as-is from the source systems. Data is ingested from CSV Files into SQL Server Database.
  2. Silver Layer: This layer includes data cleansing, standardization, and normalization processes to prepare data for analysis.
  3. Gold Layer: Houses business-ready data modeled into a star schema required for reporting and analytics.

Description

This project involves:

  1. Data Architecture: Designing a Modern Data Warehouse Using Medallion Architecture Bronze, Silver, and Gold layers.
  2. ETL Pipelines: Extracting, transforming, and loading data from source systems into the warehouse.
  3. Data Modeling: Developing fact and dimension tables optimized for analytical queries.
  4. Analytics & Reporting: Creating SQL-based reports and dashboards for actionable insights.

Project Requirements

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