Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/sanketrs/implementation-of-modern-data-engineering-architecture-with-fabric_analytics

Building a next-generation hybrid data pipeline architecture that combines the power of Microsoft Fabric, Azure Cloud, and Power BI. This pipeline is engineered to tackle the challenges of real-time data ingestion, multi-layered processing, and analytics, delivering business-critical insights.
https://github.com/sanketrs/implementation-of-modern-data-engineering-architecture-with-fabric_analytics

azure azure-data-factory azure-fabric bi-analytics big-data-analytics big-data-projects cloud-data-warehouse cloud-dataflow data-analytics data-engineering data-engineering-pipeline data-engineering-project data-pipeline-monitoring data-science data-visualization data-warehouse etl etl-framework etl-pipeline

Last synced: 12 days ago
JSON representation

Building a next-generation hybrid data pipeline architecture that combines the power of Microsoft Fabric, Azure Cloud, and Power BI. This pipeline is engineered to tackle the challenges of real-time data ingestion, multi-layered processing, and analytics, delivering business-critical insights.

Awesome Lists containing this project

README 

        
# 🚀 Building Modern Data Engineering Architecture with Fabric Analytics



## **🔥 Introduction**



This repository is your ultimate guide to building a **next-generation hybrid data pipeline architecture** that combines the power of **Microsoft Fabric**, **Azure Cloud**, and **Power BI**. This pipeline is engineered to tackle the challenges of **real-time data ingestion**, **multi-layered processing**, and **advanced analytics**, delivering **business-critical insights** at scale.



With its **Bronze-Silver-Gold architecture**, this solution enables structured data flow, ensuring:

- **High-quality data pipelines** for analytics.

- **Scalable integration** of on-premise and cloud systems.

- **Cutting-edge business intelligence** capabilities with real-time visualizations.



Whether you're a **data engineer**, **analyst**, or **business leader**, this repository equips you with the tools and architecture to unlock the **true potential of your data**.



---



## **💡 Why This Architecture Matters**



In the era of **big data**, organizations must process **diverse, high-velocity data streams** from multiple sources, including IoT devices, logs, transactional systems, and APIs. Traditional pipelines often fail to meet the demands of **scalability**, **performance**, and **cost efficiency**. 



This modern hybrid pipeline architecture addresses these pain points with:

- **🚀 Seamless Integration**: Effortlessly connects on-premise systems and cloud ecosystems.

- **⚡ Scalable Processing**: Handles high volumes of records with high efficiency using **Microsoft Fabric** and **Azure Data Lake**.

- **📊 Real-Time Analytics**: Empowers business users with actionable insights via **Power BI**.

- **💸 Cost Optimization**: Reduces infrastructure overhead with **Parquet file formats** and cloud-native services.



---



## **🔧 Hardcore Features**



### **🌐 Hybrid Ecosystem Integration**

- Combines legacy on-premise systems with modern cloud infrastructure for seamless hybrid architecture.

- Ingests data from multiple sources, including **IoT devices**, **transaction logs**, **CRM/ERP systems**, and external APIs.

- Ensures continuous synchronization between local and cloud-based systems for reliability.



### **🔽 Bronze-Silver-Gold Data Model**

1. **Bronze Layer**:

   - **Data Sources**: IoT devices, log files, weather data, customer financial transactions, and business applications feed raw data into the pipeline.

   - **Local Directory and Watchdog Service**: A monitoring mechanism ensures the ingestion of raw files, detecting changes and automating uploads to the data lake.

   - **Data Lake**: Raw data is stored in the **Parquet format**, offering efficient, high-performance storage as the first step in the pipeline.

2. **Silver Layer**:

   - **Fabric SQL Database**: Validates, cleanses, and transforms raw data into structured formats. It prepares datasets for downstream operations while maintaining data quality.

   - **Validation and Cleaning**: The database performs essential tasks like handling missing values, outliers, and applying business logic transformations.

   - **Intermediate Output**: Creates standardized datasets optimized for further enrichment.

3. **Gold Layer**:

   - **Dataflow Gen2**: Applies advanced transformations, including aggregations, relationship modeling, and creating calculated fields for specific analytics needs.

   - **Enriched Data**: Outputs analytics-ready datasets designed for direct consumption by business intelligence tools.



### **⚙️ High-Performance Data Processing**

- **Microsoft Fabric SQL Database**: Handles raw data validation and transformations efficiently with SQL-based workflows.

- **Dataflow Gen2**: Implements advanced Extract-Transform-Load (ETL) pipelines for cleaning, aggregating, and preparing data.

- **Azure Data Lake**: Offers scalable, highly durable storage for managing raw and processed datasets, making it an essential layer for hybrid architecture.



### **📈 Business-Driven Insights**

- **Fabric Data Warehouse**: Acts as the final repository for enriched datasets, optimized for large-scale analytical queries.

- **Power BI Dashboards**:

   - Enables real-time visualizations for actionable decision-making.

   - Facilitates interactive data exploration with dynamic charts and drill-down capabilities.

- Empowers business stakeholders with instant access to critical metrics and KPIs.



---



## **💪 Built for Real-World Challenges**



This architecture is designed to handle **demanding use cases** like:

- **IoT Analytics**: Processes data streams from millions of IoT devices to monitor operations and identify trends.

- **Financial Reporting**: Analyzes high-frequency transactional data to derive insights into customer behavior and revenue growth.

- **Operational Efficiency**: Optimizes workflows with insights from log data and performance metrics.

- **Environmental Impact Analysis**: Incorporates external datasets like weather and climate data to assess operational risks.



---



## **🔧 Key Technologies**



1. **Data Ingestion & Storage**:

   - **Parquet File Format**: Ideal for storing large datasets due to its columnar storage and compression capabilities.

   - **Azure Data Lake**: A scalable solution for managing raw and processed data across different environments.



2. **Data Processing**:

   - **Microsoft Fabric SQL Database**: Ensures accurate data validation and preparation for complex analytics.

   - **Dataflow Gen2**: Supports robust ETL workflows, making data enrichment and transformation seamless.



3. **Analytics & Reporting**:

   - **Power BI**: Industry-leading tool for creating, sharing, and embedding interactive dashboards.

   - **Fabric Data Warehouse**: Offers high-speed querying and efficient storage for aggregated datasets.



4. **Cloud Infrastructure**:

   - **Microsoft Azure**: Provides secure and globally available cloud services for hybrid data pipelines.



---



## **🔥 What Makes This Solution Hardcore**



- **End-to-End Pipeline**: Covers every stage, from raw data ingestion to real-time insights, ensuring a streamlined flow.

- **Real-Time Capabilities**: Enables rapid data processing and visualization for time-sensitive decision-making.

- **Enterprise-Grade Security**: Built with robust compliance and data governance features supported by Azure Cloud.



---



## **🚀 How to Get Started**



### **Prerequisites**

1. **Microsoft Azure Account**: Required for deploying cloud infrastructure and services.

2. **Power BI Desktop**: For designing and managing dynamic dashboards.

3. **On-Premise Data Sources**: Integrate IoT devices, log files, CRM/ERP systems, and external data APIs.



### **Deployment Steps**

1. **Ingest Data**:

   - Configure a **Watchdog Service** to monitor and automate raw data ingestion.

   - Store the ingested data in the **Bronze Layer** using **Parquet format** for scalability.

2. **Process Data**:

   - Use **Microsoft Fabric SQL Database** to validate, clean, and structure raw data into the **Silver Layer**.

   - Enrich data using **Dataflow Gen2**, applying domain-specific transformations.

3. **Analyze Data**:

   - Load enriched datasets into the **Gold Layer** for analytics readiness.

   - Connect **Power BI** to generate actionable insights via interactive dashboards.



---



## **⚖️ Future Enhancements**



- **Incremental Refresh**: Add support for real-time updates to keep analytics in sync with data sources.

- **Machine Learning Integration**: Incorporate predictive analytics into the pipeline for advanced decision-making.

- **Performance Optimization**: Fine-tune database queries and ETL processes for improved throughput.

- **Data Governance**: Enhance compliance and security with detailed policies and monitoring tools.



---



## **🔗 Resources**



- [Microsoft Fabric Documentation](https://learn.microsoft.com/en-us/fabric/)

- [Power BI Official Site](https://powerbi.microsoft.com/)

- [Azure Data Lake Overview](https://azure.microsoft.com/en-us/services/data-lake/)

- [Parquet File Format](https://parquet.apache.org/)



---



## **📋 License**



This project is licensed under the **[MIT License](LICENSE)**.



---



## **🙏 Acknowledgments**



- Thanks to **Microsoft Azure and Fabric Teams** for powering this hybrid architecture.

- Gratitude to the **Power BI Team** for enabling next-level business intelligence.