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https://github.com/seifo321/microsoft-data-engineer-project

Leveraging Microsoft AZURE Services , DEVELOPING a high performance ETL pipeline that extracts and transform the BikeStores data and loads it to Azure data warehouse
https://github.com/seifo321/microsoft-data-engineer-project

azure azuresynapseanalytics databricks-notebooks dataengineering etl-automation etl-pipeline machine-learning predective-modeling sqlserver

Last synced: about 1 month ago
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Leveraging Microsoft AZURE Services , DEVELOPING a high performance ETL pipeline that extracts and transform the BikeStores data and loads it to Azure data warehouse

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README 

          
# **End-to-End Data Engineering Pipeline**



## **Table of Contents**

- [Project Overview](#project-overview)

- [Architecture](#architecture)

- [Technologies](#technologies)

- [Project Objectives](#project-objectives)

- [Data Flow](#data-flow)

- [Setup and Configuration](#setup-and-configuration)

- [Pipeline Stages](#pipeline-stages)

- [Visualization](#visualization)

- [Machine Learning Models](#machine-learning-models)

- [Folder Structure](#folder-structure)



---



## **Project Overview**

This 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.



Additionally, machine learning models are incorporated to provide **predictive analytics** and **forecasting** for improved decision-making.



---



## **Architecture**

The architecture consists of several integrated Azure services for an efficient, scalable, and secure data pipeline.



- **Azure Data Factory (ADF)**: Manages ETL pipelines.

- **Azure Synapse Analytics**: Acts as a data warehouse for storage and large-scale querying.

- **Databricks**: Enables advanced data transformation and machine learning.

- **Power BI**: Generates visual insights and dashboards.

- **Azure Machine Learning**: Supports machine learning model development and deployment.



---



## **Technologies**

This project uses the following tools and platforms:

- **SQL Server** or **Relational Databases**: Stores transactional data.

- **Azure Data Factory**: Orchestrates ETL operations.

- **Azure Databricks**: Handles large-scale data transformation and machine learning.

- **Azure Synapse Analytics**: Centralized data warehouse.

- **Power BI**: Visualization platform.

- **Azure Machine Learning**: For building and deploying predictive models.

- **Python**: Used for scripting transformations and machine learning.



---



## **Project Objectives**

1. **Data Extraction**: Pull data from structured or semi-structured sources.

2. **Data Transformation**: Clean, aggregate, and normalize data.

3. **Data Loading**: Store the processed data in a centralized data warehouse.

4. **Data Visualization**: Create dashboards for reporting and analytics.

5. **Predictive Modeling**: Leverage machine learning to forecast trends and provide insights.



---



## **Data Flow**

1. **Source (SQL, CSV, etc.)**: Data is pulled from different data sources.

2. **ETL in Azure Data Factory**: ADF orchestrates the data extraction and transformation.

3. **Data Transformation (Databricks)**: Data is processed, cleaned, and prepared for analytics.

4. **Azure Synapse Analytics**: The transformed data is loaded into Synapse for further analysis.

5. **Power BI Dashboards**: Connect to Synapse to visualize trends and insights.

6. **Machine Learning Models**: Predictive models are developed to forecast trends.



---



## **Setup and Configuration**



### **Prerequisites**

- **Azure Subscription**: Access to Azure services like Data Factory, Synapse, Databricks, and Power BI.

- **Database**: A SQL Server instance or any other source where data is stored.

- **Power BI Desktop**: For designing data visualizations.



### **Azure Resource Setup**

1. **Create SQL Database**: Import your dataset into a SQL Server.

2. **Create Azure Data Factory (ADF)**: Set up data pipelines to extract and transform data.

3. **Create Azure Synapse Analytics**: Use Synapse for data storage and querying.

4. **Create Azure Databricks**: Perform large-scale data processing and machine learning tasks.

5. **Power BI**: Design dashboards to visualize insights from the data.



---



## **Pipeline Stages**



### **1. Data Extraction (Azure Data Factory)**

- Use **ADF** to orchestrate the data extraction from various sources (SQL, CSV, API).



### **2. Data Transformation (Databricks)**

- Perform **complex transformations** using **Databricks** and **Apache Spark** for distributed data processing.



### **3. Data Loading (Azure Synapse Analytics)**

- Load the cleaned and transformed data into **Azure Synapse Analytics** for storage and analysis.



### **4. Machine Learning (Databricks)**

- Build and train machine learning models using **Databricks** and track experiments with **MLflow**.



### **5. Data Visualization (Power BI)**

- Create interactive dashboards to visualize KPIs, trends, and predictive insights.



---



## **Visualization**



### **Power BI Dashboards**:

- **Performance Overview**: Analyze KPIs like sales, revenue, and customer retention.

- **Predictive Analysis**: Use historical data to forecast trends and behaviors.

- **Inventory and Sales Insights**: Manage stock levels and predict demand.



---



## **Machine Learning Models**

- **Forecasting Models**: Predict trends based on historical data.

- **Classification Models**: Segment customers based on behavior and preferences.

- **Demand Prediction**: Optimize inventory and supply chain using demand forecasting.



---



## **Folder Structure**

```plaintext

├── datasets                # Raw and processed data files

├── notebooks               # Jupyter notebooks for data exploration and ML modeling

├── pipelines               # Azure Data Factory pipeline definitions

├── scripts                 # Python scripts for data transformation and ML

├── visuals                 # Power BI report files and dashboards

└── README.md               # Project documentation