{"id":25898085,"url":"https://github.com/nxion/sql-data-warehouse-project","last_synced_at":"2026-06-05T06:31:29.260Z","repository":{"id":280323336,"uuid":"941618971","full_name":"nxion/sql-data-warehouse-project","owner":"nxion","description":"Building a modern data warehouse with MS SQL server, ETL processes, data modeling and analyitics.","archived":false,"fork":false,"pushed_at":"2025-03-02T18:19:30.000Z","size":825,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-02T19:18:41.745Z","etag":null,"topics":["data","data-analysis","data-analytics","data-engineering","data-lakehouse","data-warehouse","datalake","datascience","etl","etl-job","medallion-architecture","ms","mssql","sql","sql-query","sql-server"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nxion.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-02T17:59:14.000Z","updated_at":"2025-03-02T18:19:33.000Z","dependencies_parsed_at":"2025-03-02T19:18:45.887Z","dependency_job_id":"638e9291-cfd9-4346-86b1-d762c8990824","html_url":"https://github.com/nxion/sql-data-warehouse-project","commit_stats":null,"previous_names":["nxion/sql-data-warehouse-project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/nxion/sql-data-warehouse-project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nxion%2Fsql-data-warehouse-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nxion%2Fsql-data-warehouse-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nxion%2Fsql-data-warehouse-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nxion%2Fsql-data-warehouse-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nxion","download_url":"https://codeload.github.com/nxion/sql-data-warehouse-project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nxion%2Fsql-data-warehouse-project/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33932048,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-05T02:00:06.157Z","response_time":120,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data","data-analysis","data-analytics","data-engineering","data-lakehouse","data-warehouse","datalake","datascience","etl","etl-job","medallion-architecture","ms","mssql","sql","sql-query","sql-server"],"created_at":"2025-03-03T00:16:49.570Z","updated_at":"2026-06-05T06:31:29.255Z","avatar_url":"https://github.com/nxion.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# My MS SQL Data Warehouse Project and Analysis\nHello to all who read this! This project demonstrates a comprehensive data warehousing and analytics solution, from building a data warehouse to generating actionable insights. \n\n---\n## 📚 Project Overview\nSince this is designed as a portfolio project, it highlights industry’s best practices in data engineering and analytics. The project involves:\n* Data Architecture: Design a Data Warehouse using the medallion method (Bronze, Silver, and Gold layers).\n* ETL Pipelines: Extraction, transformation and loading of data from a source system into our warehouse.\n* Data Modeling: Develop a fact and dimension tables to optimize analytical queries\n* Analytics \u0026 Reporting: Creating SQL based reports for actionable insights.\n\n## 💡 Alternatives Database Systems\nThis portfolio project was based off using Microsoft SQL Server since it is a common industry standard. I will also be implementing in PostgreSQL, MariaDB, MySQL and Google BigQuery to showcase my adaptation of other database technologies.\n\n## 🦺 Project Expansion\nWhile not part of the original project. I thought of a AI powered chat interface that could query the data and action results using NLP and a simple chat interface. More to come...\n\n---\n## 🏗️ Data Architecture\nThe data architecture for this project follows Medallion Architecture Bronze, Silver, and Gold layers:\n![Data Architecture](docs/data_architecture.drawio.png)\n\n* 🥉 **Bronze Layer**: Stores raw data as-is from the source systems. Data is ingested from CSV Files into SQL Server Database.\n* 🥈 **Silver Layer**: This layer includes data cleansing, standardization, and normalization processes to prepare data for analysis.\n* 🥇 **Gold Layer**: Houses business-ready data modeled into a star schema required for reporting and analytics.\n---\n## 📋 Project Requirements\n### Building the Data Warehouse (Data Engineering)\n#### Objective\n* Develop a modern data warehouse using SQL Server to consolidate sales data, enabling analytical reporting and informed decision-making.\n\n#### Specifications\n* **Data Sources**: Import data from two source systems (ERP and CRM) provided as CSV files.\n* **Data Quality**: Cleanse and resolve data quality issues prior to analysis.\n* **Integration**: Combine both sources into a single, user-friendly data model designed for analytical queries.\n* **Scope**: Focus on the latest dataset only; historization of data is not required.\n* **Documentation**: Provide clear documentation of the data model to support both business stakeholders and analytics teams.\n\n### BI: Analytics \u0026 Reporting (Data Analysis)\n#### Objective\n\n* Develop SQL-based analytics to deliver detailed insights into:\n    1. Customer Behavior\n    2. Product Performance\n    3. Sales Trends\n\nThese insights empower stakeholders with key business metrics, enabling strategic decision-making.\n\nAdditional requirements, naming conventions and tables descriptions with details will be listed in the [docs folder](docs) of this project.\n\n---\n\nThis project was created by [Data with Baraa](https://www.youtube.com/@DataWithBaraa). It was a wonderful project, and I learned a considerable amount. In my professional life I have done projects like this, but it was something that I was never formally trained in. My background is more Infrastructure Technology Specialist than Data focused. This project has encouraged me to do more projects related to data analysis/engineering. All credit should go to him. A link to [this project](https://github.com/DataWithBaraa/sql-data-warehouse-project) and his [Github](https://github.com/DataWithBaraa) are here.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnxion%2Fsql-data-warehouse-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnxion%2Fsql-data-warehouse-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnxion%2Fsql-data-warehouse-project/lists"}