{"id":17538631,"url":"https://github.com/seifo321/microsoft-data-engineer-project","last_synced_at":"2026-05-07T13:33:59.041Z","repository":{"id":258415721,"uuid":"866133322","full_name":"Seifo321/Microsoft-Data-Engineer-Project","owner":"Seifo321","description":"Leveraging Microsoft AZURE Services , DEVELOPING a high performance ETL pipeline that extracts and transform the BikeStores data and loads it to Azure data warehouse ","archived":false,"fork":false,"pushed_at":"2025-05-26T11:50:40.000Z","size":8451,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-26T12:46:03.076Z","etag":null,"topics":["azure","azuresynapseanalytics","databricks-notebooks","dataengineering","etl-automation","etl-pipeline","machine-learning","predective-modeling","sqlserver"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Seifo321.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2024-10-01T17:48:39.000Z","updated_at":"2025-05-26T11:50:43.000Z","dependencies_parsed_at":"2025-05-26T12:45:36.863Z","dependency_job_id":null,"html_url":"https://github.com/Seifo321/Microsoft-Data-Engineer-Project","commit_stats":null,"previous_names":["seifo321/microsoft-data-engineer-project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Seifo321/Microsoft-Data-Engineer-Project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Seifo321%2FMicrosoft-Data-Engineer-Project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Seifo321%2FMicrosoft-Data-Engineer-Project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Seifo321%2FMicrosoft-Data-Engineer-Project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Seifo321%2FMicrosoft-Data-Engineer-Project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Seifo321","download_url":"https://codeload.github.com/Seifo321/Microsoft-Data-Engineer-Project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Seifo321%2FMicrosoft-Data-Engineer-Project/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268016829,"owners_count":24181655,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-07-31T02:00:08.723Z","response_time":66,"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":["azure","azuresynapseanalytics","databricks-notebooks","dataengineering","etl-automation","etl-pipeline","machine-learning","predective-modeling","sqlserver"],"created_at":"2024-10-20T21:03:30.617Z","updated_at":"2026-05-07T13:33:58.959Z","avatar_url":"https://github.com/Seifo321.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# **End-to-End Data Engineering Pipeline**\n\n\n## **Table of Contents**\n- [Project Overview](#project-overview)\n- [Architecture](#architecture)\n- [Technologies](#technologies)\n- [Project Objectives](#project-objectives)\n- [Data Flow](#data-flow)\n- [Setup and Configuration](#setup-and-configuration)\n- [Pipeline Stages](#pipeline-stages)\n- [Visualization](#visualization)\n- [Machine Learning Models](#machine-learning-models)\n- [Folder Structure](#folder-structure)\n\n---\n\n## **Project Overview**\nThis project aims to build an end-to-end data engineering pipeline designed to extract, transform, and load (ETL) data into a central data warehouse for analysis and insights. The project integrates with cloud-based solutions such as **Azure Data Factory** for orchestrating pipelines, **Azure Synapse Analytics** for data storage and querying, and **Power BI** for visualization.\n\nAdditionally, machine learning models are incorporated to provide **predictive analytics** and **forecasting** for improved decision-making.\n\n---\n\n## **Architecture**\nThe architecture consists of several integrated Azure services for an efficient, scalable, and secure data pipeline.\n\n\n- **Azure Data Factory (ADF)**: Manages ETL pipelines.\n- **Azure Synapse Analytics**: Acts as a data warehouse for storage and large-scale querying.\n- **Databricks**: Enables advanced data transformation and machine learning.\n- **Power BI**: Generates visual insights and dashboards.\n- **Azure Machine Learning**: Supports machine learning model development and deployment.\n\n---\n\n## **Technologies**\nThis project uses the following tools and platforms:\n- **SQL Server** or **Relational Databases**: Stores transactional data.\n- **Azure Data Factory**: Orchestrates ETL operations.\n- **Azure Databricks**: Handles large-scale data transformation and machine learning.\n- **Azure Synapse Analytics**: Centralized data warehouse.\n- **Power BI**: Visualization platform.\n- **Azure Machine Learning**: For building and deploying predictive models.\n- **Python**: Used for scripting transformations and machine learning.\n\n---\n\n## **Project Objectives**\n1. **Data Extraction**: Pull data from structured or semi-structured sources.\n2. **Data Transformation**: Clean, aggregate, and normalize data.\n3. **Data Loading**: Store the processed data in a centralized data warehouse.\n4. **Data Visualization**: Create dashboards for reporting and analytics.\n5. **Predictive Modeling**: Leverage machine learning to forecast trends and provide insights.\n\n---\n\n## **Data Flow**\n1. **Source (SQL, CSV, etc.)**: Data is pulled from different data sources.\n2. **ETL in Azure Data Factory**: ADF orchestrates the data extraction and transformation.\n3. **Data Transformation (Databricks)**: Data is processed, cleaned, and prepared for analytics.\n4. **Azure Synapse Analytics**: The transformed data is loaded into Synapse for further analysis.\n5. **Power BI Dashboards**: Connect to Synapse to visualize trends and insights.\n6. **Machine Learning Models**: Predictive models are developed to forecast trends.\n\n---\n\n## **Setup and Configuration**\n\n### **Prerequisites**\n- **Azure Subscription**: Access to Azure services like Data Factory, Synapse, Databricks, and Power BI.\n- **Database**: A SQL Server instance or any other source where data is stored.\n- **Power BI Desktop**: For designing data visualizations.\n\n### **Azure Resource Setup**\n1. **Create SQL Database**: Import your dataset into a SQL Server.\n2. **Create Azure Data Factory (ADF)**: Set up data pipelines to extract and transform data.\n3. **Create Azure Synapse Analytics**: Use Synapse for data storage and querying.\n4. **Create Azure Databricks**: Perform large-scale data processing and machine learning tasks.\n5. **Power BI**: Design dashboards to visualize insights from the data.\n\n---\n\n## **Pipeline Stages**\n\n### **1. Data Extraction (Azure Data Factory)**\n- Use **ADF** to orchestrate the data extraction from various sources (SQL, CSV, API).\n\n### **2. Data Transformation (Databricks)**\n- Perform **complex transformations** using **Databricks** and **Apache Spark** for distributed data processing.\n\n### **3. Data Loading (Azure Synapse Analytics)**\n- Load the cleaned and transformed data into **Azure Synapse Analytics** for storage and analysis.\n\n### **4. Machine Learning (Databricks)**\n- Build and train machine learning models using **Databricks** and track experiments with **MLflow**.\n\n### **5. Data Visualization (Power BI)**\n- Create interactive dashboards to visualize KPIs, trends, and predictive insights.\n\n---\n\n## **Visualization**\n\n### **Power BI Dashboards**:\n- **Performance Overview**: Analyze KPIs like sales, revenue, and customer retention.\n- **Predictive Analysis**: Use historical data to forecast trends and behaviors.\n- **Inventory and Sales Insights**: Manage stock levels and predict demand.\n\n---\n\n## **Machine Learning Models**\n- **Forecasting Models**: Predict trends based on historical data.\n- **Classification Models**: Segment customers based on behavior and preferences.\n- **Demand Prediction**: Optimize inventory and supply chain using demand forecasting.\n\n---\n\n## **Folder Structure**\n```plaintext\n├── datasets                # Raw and processed data files\n├── notebooks               # Jupyter notebooks for data exploration and ML modeling\n├── pipelines               # Azure Data Factory pipeline definitions\n├── scripts                 # Python scripts for data transformation and ML\n├── visuals                 # Power BI report files and dashboards\n└── README.md               # Project documentation\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fseifo321%2Fmicrosoft-data-engineer-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fseifo321%2Fmicrosoft-data-engineer-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fseifo321%2Fmicrosoft-data-engineer-project/lists"}