• Unified Data Management – Centralised storage, governance, and access control ensure security and compliance. 
• Real-Time Analytics – Process and analyse data from multiple sources with low latency. 
• Advanced Data Engineering – Databricks integration supports distributed processing and machine learning. 
• Scalability Without Waste – Elastic compute and storage scale efficiently to optimise costs. 

Microsoft Fabric’s modular design empowers enterprises to build a future-proof data strategy. Its core components work together seamlessly, enabling advanced data operations at scale. 

Core Components: 

1. Data Factory:
   1. Native connectors for diverse data sources (on-premises, cloud, real-time streams).
   2. Low-code/no-code pipeline design for rapid development. 
   3. Orchestration and automation for consistent data delivery
2. Data Engineering:
   1. Medallion Architecture for Bronze, Silver, and Gold layers, ensuring data consistency.
   2. Databricks Integration with Apache Spark for scalable data processing and ML workflows.
   3. Delta Lake Compatibility for efficient file management and ACID transactions.
3. Lakehouse Data Warehousing: 
   1. Combines data lake scalability with data warehouse performance. 
   2. DirectQuery and In-Memory Caching for low-latency analytics.
   3. Support for T-SQL and hybrid query engines for maximum flexibility. 
4. OneLake Unified Storage: 
   1. Centralized data storage layer supporting structured and unstructured data. 
   2. Fine-grained access controls for secure data sharing across teams. 
   3. Integrated AI and Machine Learning:
   4. Built-in notebooks for data exploration and prototyping.
   5. Azure Machine Learning Integration for scalable model training and deployment.
   6. MLOps capabilities for consistent model monitoring and governance.
5. Power BI Integration: 
   1. Direct connectivity to OneLake and data warehouses for seamless reporting.
   2. AI-driven analytics and natural language querying for advanced insights.
   3. Enterprise-Grade Security and Governance:
   4. Role-Based Access Control (RBAC) for secure data management.
   5. Comprehensive data lineage tracking and governance policies.
   6. Support for industry compliance standards like GDPR and SOC 2.

• Unified Platform: Consolidate data engineering, analytics, and governance into one ecosystem. 
• Scalable and Performant: Built on Azure’s cloud infrastructure, enabling elastic scalability and high-performance analytics. 
• End-to-End Integration: From ingestion to actionable insights, Fabric streamlines complex data workflows. 
• Future-Proof Architecture: Modular and agile design that evolves with your data strategy. 
• Cost Efficiency: Reduce complexity and operational costs by eliminating the need for multiple tools and systems. 

1. Data Ingestion and Orchestration (Data Factory) 
   1. Connect: Securely connect to a wide range of data sources, including on-premises systems, cloud databases, APIs, and streaming data. 
   2. Ingest: Use native connectors and pipelines to ingest raw data into the Bronze Layer in OneLake, maintaining the original state for traceability. 
   3. Automate: Orchestrate end-to-end data workflows, automating data movement with event-driven triggers. 
2. Data Processing and Transformation (Data Engineering / Data Warehousing) 
   1. Medallion Architecture: Implement the Medallion Architecture (Bronze → Silver → Gold) to ensure data consistency, quality, and scalability. 
   2. Clean and Transform: Process raw data in the Bronze Layer to create structured, cleaned datasets in the Silver Layer using Apache Spark. 
   3. Enrich and Aggregate: Apply business logic and enrich data, preparing it for analytics in the Gold Layer.
3. Storage and Data Lakehouse (OneLake) 
   1. Centralized Storage: Store all layers (Bronze, Silver, Gold) in OneLake, providing unified access to structured and unstructured data. 
   2. Data Lakehouse Approach: Combine data lake scalability with the query performance of a traditional data warehouse. 
   3. Delta Lake Compatibility: Use Delta Lake tables for ACID transactions, versioning, and efficient querying. 
4. Advanced Analytics and Machine Learning (Azure ML) 
   1. Exploratory Data Analysis: Utilize Data Warehousing/Data Engineering capabilities for interactive data exploration and prototyping. 
   2. Machine Learning Pipelines: Develop and train ML models using Azure Machine Learning, integrated seamlessly with Databricks. 
   3. MLOps Integration: Deploy models with CI/CD pipelines and monitor them for accuracy and drift. 
5. Business Intelligence and Reporting (Power BI) 
   1. 1. Direct Integration: Connect Power BI directly to OneLake and Lakehouse data warehouses for live dashboards and reports. 
      2. Data Modeling and Visualization: Build semantic models and interactive visualizations for real-time insights. 
      3. AI-Powered Analytics: Leverage Power BI’s AI capabilities for advanced analytics, including natural language querying and anomaly detection. 
6. Data Governance and Security (Fabric Security + Compliance) 
   1. 1. 1. Unified Governance: Manage data access, lineage, and security policies across the platform. 
         2. Role-Based Access Control (RBAC): Ensure secure and compliant data usage with granular RBAC settings. 
         3. Data Lineage and Auditing: Track data lineage end-to-end, supporting compliance with regulations like GDPR and SOC 2.