https://github.com/chojogebutsu/postgresql-data-warehouse-project
Building a Data Warehouse with PostgreSQL, including ETL processes, Data Modeling, and Analytics.
https://github.com/chojogebutsu/postgresql-data-warehouse-project
data-cleaning data-engineering data-warehouse etl plpgsql postgresql sql
Last synced: about 1 year ago
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Building a Data Warehouse with PostgreSQL, including ETL processes, Data Modeling, and Analytics.
- Host: GitHub
- URL: https://github.com/chojogebutsu/postgresql-data-warehouse-project
- Owner: ChojoGebutsu
- License: mit
- Created: 2025-01-29T09:40:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-20T06:46:19.000Z (over 1 year ago)
- Last Synced: 2025-03-12T18:38:05.689Z (over 1 year ago)
- Topics: data-cleaning, data-engineering, data-warehouse, etl, plpgsql, postgresql, sql
- Language: PLpgSQL
- Homepage:
- Size: 1.61 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# PostgreSQL Data Warehouse Project
Welcome to the **PostgreSQL Data Warehouse Project** repository! 🚀
This project exemplifies a robust data warehousing and analytics solution, encompassing the building of a data warehouse and the generation of actionable insights. Designed as a portfolio piece, it demonstrates industry-leading practices in data engineering and analytics.
---
## 📖 Project Overview
This project encompasses:
1. **Data Architecture**: Architecting a modern data warehouse utilizing the Medallion Architecture, structured into **Bronze**, **Silver**, and **Gold** layers.
2. **ETL Pipelines**: Extracting, transforming, and loading data from disparate source systems into the warehouse.
3. **Data Modeling**: Crafting fact and dimension tables optimized for analytical querying.
4. **Analytics & Reporting**: Developing SQL-based reports and dashboards to derive actionable insights.
---
## 🛠️ Tools I Used
Everything is free!
- **[Datasets](datasets/)**: Access the project datasets (CSV files).
- **[PostgreSQL](https://www.postgresql.org/download/)**: Open-source relational database for hosting your data warehouse.
- **[VS Code](https://code.visualstudio.com/download)**: A go-to GUI for PostgreSQL database management and executing SQL queries.
- **[Notion Project Steps](https://shorturl.at/TIc1c)**: Access all project phases and tasks.
---
## 🚀 Project Requirements
### Building the Data Warehouse (Data Engineering)
#### Objective
Construct a modern data warehouse using PostgreSQL to consolidate sales data, facilitating 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**: Merge both sources into a single, user-friendly data model designed for analytical queries.
- **Scope**: Focus exclusively on the latest dataset; historization of data is not required.
- **Documentation**: Provide comprehensive 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 granular insights into:
- **Customer Behavior**
- **Product Performance**
- **Sales Trends**
These insights empower stakeholders with critical business metrics, enabling strategic decision-making.
---
## 🏗️ Data Architecture
The data architecture for this project adheres to the Medallion Architecture, comprising **Bronze**, **Silver**, and **Gold** layers:
1. **Bronze Layer**: Stores raw data in its original form from the source systems. Data is ingested from CSV files into the PostgreSQL database.
2. **Silver Layer**: Involves data cleansing, standardization, and normalization processes to prepare data for analysis.
3. **Gold Layer**: Hosts business-ready data modeled into a star schema for reporting and analytics.
---
---
## 📂 Repository Structure
```
PostgreSQL_Data_Warehouse_Project/
│
├── datasets/ # Raw datasets used for the project (ERP and CRM data)
│
├── docs/ # Project documentation and architecture details
│ ├── ETL_process.png # A diagram created in Draw.io showing ETL techniques and methods used in this project
│ ├── data_architecture.png # A diagram created in Draw.io showing the project's architecture
│ ├── data_catalogue.md # Catalog of datasets, including field descriptions and metadata
│ ├── data_flow.png # A diagram created in Draw.io for the data flow diagram
│ ├── data_integration_model.png # A diagram created in Draw.io that shows how tables are related to each other.
│ ├── gold_layer_data_model.png # A diagram created in Draw.io that shows gold layer data model (star schema)
│ ├── naming-conventions.md # Consistent naming guidelines for tables, columns, and files
│
├── scripts/ # SQL scripts for ETL and transformations
│ ├── bronze/ # Scripts for extracting and loading raw data
│ ├── silver/ # Scripts for cleaning and transforming data
│ ├── gold/ # Scripts for creating analytical models
│
├── tests/ # Test scripts and quality files
│
├── README.md # Project overview and instructions
├── LICENSE # License information for the repository
├── .gitignore # Files and directories to be ignored by Git
```
---
## 🛡️ License
This project is licensed under the [MIT License](LICENSE). You are free to use, modify, and share this project with proper attribution.