Welcome to the Hybrid Data Pipeline Architecture repository. This project demonstrates how to build a scalable, efficient, and hybrid data pipeline using Microsoft Fabric, Azure Cloud services, and Power BI. By integrating on-premise ecosystems with the cloud, this architecture enables seamless data ingestion, transformation, and analytics. The pipeline follows a structured Bronze-Silver-Gold data model to ensure high-quality data readiness for advanced analytics and business reporting.
In today’s data-driven world, organizations must manage and process vast amounts of data from diverse sources like IoT devices, financial transactions, weather systems, and business applications. This hybrid architecture offers:
- End-to-End Data Automation: From ingestion to analytics.
- Scalable Data Management: Handles growing datasets efficiently.
- Real-Time Insights: Delivers actionable insights through Power BI dashboards.
- Cost Optimization: Uses Parquet format and cloud-native tools to minimize costs.
This repository is perfect for businesses, data engineers, and analysts looking to modernize their data pipelines and adopt cloud-driven analytics.
This architecture bridges on-premise systems with cloud-based analytics to provide an end-to-end data processing pipeline. The system follows a layered structure:
- Bronze Layer: Stores raw, unprocessed data.
- Silver Layer: Contains cleaned and validated data.
- Gold Layer: Houses enriched and analytics-ready datasets.
- IoT Devices: Real-time data collection from connected sensors.
- Logs & Files: Includes system logs, event data, and file-based inputs.
- Customer & Financial Data: Extracted from CRM, ERP, and other transactional systems.
- Weather Data: Ingests environmental data for impact analysis.
- Business Applications: Data from enterprise apps like SAP, Salesforce, and custom solutions.
- A staging area for incoming raw data.
- Enables quick ingestion and temporary storage.
- Monitors the local directory for new or modified files.
- Automates triggers for data ingestion, minimizing manual intervention.
- Stores raw data in Parquet format, ensuring:
- Cost-effective storage.
- High performance for downstream processes.
- Validates and cleans raw data from the Bronze Layer.
- Applies transformations to create structured datasets.
- Stores intermediate validated datasets for further enrichment.
- Conducts advanced data transformations, such as:
- Aggregations.
- Derived metrics and calculated fields.
- Establishing relationships between datasets.
- Produces business-ready datasets for analytics.
- A high-performance storage solution for enriched data.
- Supports large-scale analytical queries and reporting.
- Connects seamlessly with the Fabric Data Warehouse.
- Provides interactive dashboards and real-time visualizations.
- Empowers business users with actionable insights.
- Purpose: Stores raw, unprocessed data.
- Advantages:
- Acts as a historical archive.
- Retains original data for reprocessing if needed.
- Purpose: Cleansed and structured data.
- Advantages:
- Ensures accuracy, consistency, and reliability.
- Prepares data for complex transformations.
- Purpose: Business-ready, enriched datasets.
- Advantages:
- Optimized for Power BI dashboards and advanced analytics.
- Supports decision-making with aggregated insights.
- Parquet File Format: Optimized for large datasets and cloud storage.
- Azure Data Lake: Scalable storage for raw data.
- Microsoft Fabric SQL Database: For validation and cleansing.
- Dataflow Gen2: Advanced ETL (Extract, Transform, Load) capabilities.
- Fabric Data Warehouse: Centralized repository for enriched data.
- Power BI: Industry-leading tool for data visualization and business intelligence.
- Microsoft Azure: Reliable, secure, and scalable cloud infrastructure.
- Combines on-premise and cloud ecosystems.
- Bridges legacy systems with modern cloud-based analytics.
- Watchdog service automates data ingestion, reducing manual errors.
- Handles increasing data volumes with efficient processing and storage solutions.
- Structured approach (Bronze-Silver-Gold) ensures high-quality data.
- Parquet format and Azure services reduce storage and processing costs.
- Power BI provides real-time insights, enabling data-driven decision-making.
- IoT Analytics: Monitor sensor data to improve operations.
- Financial Reporting: Analyze transactional trends and customer behavior.
- Weather Impact Analysis: Assess environmental influences on business performance.
- Operational Efficiency: Gain insights from log data and business applications.
- Azure Account: For deploying cloud resources.
- Power BI Desktop: For building dashboards.
- On-Premise Data Sources: Ensure data availability from IoT devices, CRM, ERP, etc.
- Configure a local directory and the Watchdog service for data ingestion.
- Deploy a Data Lake for storing raw data in Parquet format.
- Set up Fabric SQL Database for data validation and transformation.
- Use Dataflow Gen2 to enrich data into the Gold Layer.
- Connect Power BI to the Fabric Data Warehouse for visualization.
- Add incremental refresh to pipelines for real-time updates.
- Optimize Fabric SQL Database for faster query performance.
- Integrate machine learning models for predictive analytics.
- Explore Data Governance solutions for enhanced security.
This project is licensed under the MIT License.
- Microsoft Fabric Documentation
- Power BI Official Site
- Azure Data Lake Overview
- Parquet Format Documentation
- Microsoft Azure and Fabric Teams for enabling hybrid architecture.
- Power BI Team for providing robust visualization tools.
- All contributors and stakeholders who made this project possible.