Shashi Shankar
Mar 17, 2023
All you need to know about Microsoft Azure Fabric
What is Microsoft Fabric
Microsoft Fabric is a comprehensive analytics solution designed for enterprises, providing a unified platform that encompasses data movement, data science, real-time analytics, and business intelligence. This all-in-one solution integrates a wide range of services, including data lake management, data engineering, and data integration, thus, creating a seamless environment for analytics workflows.
Microsoft Fabric, offered as a Software as a Service (SaaS), seamlessly integrates components from Power BI, Azure Synapse Analytics, Azure Data Explorer (formerly known as KustoDB), Azure Data Factory, and Azure Data Lake Storage. This integration enables organizations to harness the full potential of their data assets
Microsoft Fabric simplifies data management and enhances security, providing a unified experience for seamless collaboration and efficient workflows.
With Microsoft Fabric, IT teams can centrally configure core enterprise capabilities, ensuring consistent permissions across all underlying services. It enables the convenience of automatic inheritance of data sensitivity labels, ensuring consistent application across all components of the suite
Components of Microsoft Fabrics
Microsoft Fabric integrates a comprehensive array of services including Data Factory, Synapse Data Engineering, Synapse Data Science, Synapse Data Warehousing, Synapse Real-time Analytics, Data Activator, and Power BI Copilot.
Fabric enables organizations to leverage serverless processing, enabling streamlined execution of SQL, Spark, and KSQL queries for enhanced operational efficiency.
Data Factory
Azure Data Factory offers robust, pre-configured data orchestration capabilities
Simplifies development of flexible data workflows tailored to specific needs
Cloud-based service enables design, scheduling, and oversight of data pipelines
Supports ingestion, transformation, and loading of data from various sources
Sources include real-time streams, databases, data warehouses, structured, unstructured, and semi-structured data, and data lakes
Data Factory designer provides approximately 300 transformation functionalities
Includes AI-powered capabilities for advanced data manipulations
Some key features of Azure Data Factory include:
Data Movement:
Azure Data Factory supports smooth data migration across on-premises and cloud-based repositories
Compatible with diverse data repositories such as Azure Blob Storage, Azure SQL Database, Azure Synapse Analytics, SQL Server, Amazon S3, among others
Facilitates both batch and real-time data movement scenarios
Built-in activities for data transformation including mapping, filtering, aggregation, and sorting
Leverages distributed processing frameworks like Azure Databricks, HDInsight, or Azure Synapse Analytics for scalable data transformation operations
Orchestration:
Azure Data Factory provides a graphical user interface (GUI) for creating data workflows
Users can establish dependencies among activities within the workflows
Configurable triggers based on time or events can be set up
Centralized monitoring dashboard enables oversight of data pipeline execution
Integration with Azure Services:
Azure Data Factory integrates seamlessly with various Azure services
Supported services include Azure Synapse Analytics, Azure Machine Learning, Azure Data Lake Storage, Azure SQL Database, among others
Integration enables users to leverage additional capabilities
Additional capabilities include advanced analytics, machine learning, and storage.
Monitoring and Management:
Azure Data Factory seamlessly integrates with Azure services such as Azure Synapse Analytics, Azure Machine Learning, Azure Data Lake Storage, and Azure SQL Database
Integration enables users to access supplementary functionalities
Supplementary functionalities include advanced analytics, machine learning, and storage capabilities
Security and Compliance:
Azure Data Factory ensures data security through encryption, authentication, and authorization mechanisms
Security measures apply to both transit and at-rest data
Facilitates compliance with GDPR, HIPAA, and ISO standards
Supports hybrid data integration by enabling connections to on-premises data sources
Utilizes self-hosted integration runtimes for on-premises connectivity
Allows organizations to leverage existing infrastructure alongside cloud-based data integration capabilities
Synapse Data Engineering:
Azure Synapse Data Engineering enables creation of scalable, reliable, and cost-effective data processing solutions
Allows organizations to derive actionable insights from data reservoirs
Empowers businesses to accelerate time-to-insight and drive innovation
Utilizes big data analytics capabilities
Azure Synapse Data Engineering is a Microsoft Azure service
Facilitates development, deployment, and management of big data processing and analytics solutions
Primarily handles large volumes of structured and unstructured data
Enables organizations to ingest, transform, and analyze data at scale
Key features and capabilities of Azure Synapse Data Engineering include:
Data Ingestion:
Synapse Data Engineering enables efficient data ingestion from diverse sources
Supported sources include databases, data lakes, streaming sources, and external systems
Supports both real-time and batch data ingestion mechanisms
Ensures continuous and reliable data flow
Data Transformation:
Provides powerful tools and frameworks for transforming raw data into insights
Users can leverage distributed processing engines like Apache Spark and SQL
Supports complex data transformations, aggregations, and calculations on large datasets
Offers seamless integration with other Azure services
Supported services include Azure Data Lake Storage, Azure SQL Database, Azure Blob Storage, and Azure Data Factory
Enables orchestration of data workflows and integration with existing data pipelines
Scalability and Performance:
Azure Synapse Data Engineering is designed for handling massive workloads
Scales dynamically based on demand
Leverages distributed computing and parallel processing capabilities
Delivers high performance and processing speeds for data-intensive tasks
Security and Compliance:
Incorporates robust security features and compliance controls
Ensures confidentiality, integrity, and availability of data
Supports encryption, access controls, auditing, and compliance certifications
Meets regulatory requirements and industry standards
Analytics and Visualization:
Integrates with Azure Synapse Analytics and Power BI
Provides advanced analytics and data visualization capabilities
Users can gain valuable insights from their data
Offers built-in analytics tools
Enables creation of interactive dashboards and reports for decision-making
Synapse Data Science:
Azure Synapse Data Science is a cloud-based service by Microsoft Azure
Enables collaboration, building, training, and deployment of machine learning models at scale
Streamlines end-to-end process of developing and operationalizing machine learning solutions
Covers data preparation, exploration, model training, and deployment stages
Key features and capabilities of Azure Synapse Data Science include:
Unified Analytics Platform:
Azure Synapse Data Science offers a unified platform for data preparation, exploration, modeling, and deployment
Seamlessly integrates with Azure Synapse Analytics
Enables users to leverage both SQL-based analytics and advanced machine learning capabilities within the same environment
Scalable Machine Learning:
Offers built-in support for popular machine learning frameworks and libraries
Includes TensorFlow, PyTorch, scikit-learn, and Spark ML
Enables training of machine learning models at scale
Utilizes distributed processing capabilities for handling large datasets and complex algorithms efficiently
Collaborative Development:
Facilitates collaboration among data scientists, analysts, and developers
Includes features such as shared notebooks, version control, and project management tools
Teams can collaborate on data science projects
Enables sharing of code and insights
Tracks changes to models and experiments
Automated Machine Learning:
Includes automated machine learning (AutoML) capabilities
Enables quick building and deployment of machine learning models
Automates feature engineering, model selection, and hyperparameter tuning
Allows users to focus on solving business problems rather than fine-tuning algorithms
Model Deployment and Management:
Supports seamless deployment and management of machine learning models in production environments
Enables deployment as web services or batch scoring jobs
Facilitates monitoring of model performance and usage
Allows integration into existing applications and workflows
Integration with Azure Services:
Integrates with Azure Machine Learning, Azure Data Lake Storage, Azure Databricks, and Azure SQL Database
Enables leveraging additional capabilities for data preparation, model training, and deployment
Allows taking advantage of scalability, security, and compliance features of the Azure platform
Azure Synapse Analytics:
Azure Synapse Analytics, formerly Azure Synapse Data Warehouse, is a cloud-based analytics service by Microsoft Azure
Brings together big data and data warehousing into a single, unified platform
Enables organizations to analyze and gain insights from large volumes of data across various sources
Azure Synapse Analytics is used for several purposes:
Data Warehousing:
Serves as a centralized repository for structured and semi-structured data from different sources
Enables storing massive amounts of data in a scalable and cost-effective manner
Makes data accessible for analytics and reporting purposes
Big Data Analytics:
Provides built-in support for processing and analyzing big data
Utilizes distributed computing technologies like Apache Spark and SQL on-demand
Enables running complex queries and performing advanced analytics
Allows deriving valuable insights from large datasets without specialized infrastructure or expertise
Data Integration:
Offers seamless integration with Azure services like Azure Data Lake Storage, Azure Blob Storage, Azure Data Factory, and Azure Machine Learning
Enables ingestion, transformation, and integration of data from various sources into the data warehouse
Creates a unified view of data for analysis
Advanced Analytics and AI:
Includes advanced analytics and artificial intelligence (AI) capabilities
Supports predictive modeling, machine learning, and real-time analytics
Enables users to leverage built-in algorithms, models, and tools
Facilitates predictive analysis, anomaly detection, and sentiment analysis on data
Scalability and Performance:
Built on massively parallel processing (MPP) architecture
Scales dynamically based on workload demands
Provides high performance and low-latency querying
Enables quick and efficient analysis of large datasets
Security and Compliance:
Includes robust security features and compliance controls
Protects sensitive data and ensures regulatory compliance
Supports encryption, access controls, auditing, and compliance certifications
Meets industry standards and regulatory requirements
Synapse Real-time Analytics:
Azure Synapse Real-Time Analytics is a fully-managed, feature-rich big data analytics platform
Tailored for processing streaming and time-series data
Seamlessly integrated within the entire suite of Fabric products
Enables smooth data ingestion, transformation, and advanced visualization workflows
Handles structured, semi-structured, and unstructured data
Provides flexibility and versatility
Azure Synapse Real-time Analytics is used for several purposes:
Real-time Data Ingestion:
Allows ingestion of data from various streaming sources
Supported sources include IoT devices, sensors, social media feeds, clickstream data, among others
Supports high-throughput, low-latency data ingestion
Ensures quick processing and analysis of data
Streaming Data Processing:
Provides tools and frameworks for processing streaming data in real-time
Users can perform transformations, aggregations, enrichments, and other operations on data streams
Utilizes technologies such as Apache Spark Streaming, Azure Stream Analytics, and Azure Functions
Complex Event Processing:
Supports complex event processing (CEP) for detecting patterns, trends, and anomalies in streaming data
Enables users to define rules, queries, and patterns
Identifies meaningful events and triggers actions or alerts in response to specific conditions
Real-time Analytics and Visualization:
Enables real-time analytics and visualization on streaming data
Users can create dashboards, reports, and visualizations
Monitors and analyzes streaming data streams in real-time
Facilitates gaining insights and making informed decisions quickly
Integration with Azure Services:
Seamlessly integrates with other Azure services like Azure Data Lake Storage, Azure Blob Storage, Azure Event Hubs, Azure IoT Hub, and Azure Machine Learning
Enables users to leverage additional capabilities for data storage, data processing, and machine learning in their real-time analytics workflows
Scalability and Performance:
Built on a scalable and distributed architecture
Scales dynamically based on workload demands
Provides high throughput and low-latency processing of streaming data streams
Enables users to handle large volumes of data efficiently
Azure Synapse Analytics:
Overarching platform for data analytics, including real-time analytics
Encompasses all services and tools for data analytics
Provides a unified environment for ingesting, processing, analyzing, and visualizing data
Supports data from various sources
Azure Stream Analytics:
Azure Stream Analytics is a real-time event processing engine
Analyzes and processes streaming data from sources like IoT devices, sensors, and social media feeds
Supports complex event processing (CEP) and SQL-like queries
Capable of filtering, aggregating, and transforming data in real-time
Integration with Azure Event Hubs:
Azure Event Hubs is a scalable event ingestion service
Can receive and process millions of events per second
Seamlessly integrates with Azure Synapse Real-Time Analytics
Ingests streaming data from various sources
Routes data to downstream processing systems
Integration with Azure IoT Hub:
Azure IoT Hub is a managed service for connecting, monitoring, and managing IoT devices
Azure Synapse Real-Time Analytics can integrate with Azure IoT Hub
Ingests telemetry data from IoT devices in real-time
Enables analysis and processing of data
Integration with Azure Databricks:
Azure Databricks is a managed Apache Spark-based analytics platform
Used for big data processing and machine learning
Azure Synapse Real-Time Analytics can leverage Azure Databricks
Enables performing advanced analytics and machine learning on streaming data streams
Integration with Azure Data Lake Storage:
Azure Data Lake Storage is a scalable and secure data lake service
Used for storing and managing big data
Azure Synapse Real-Time Analytics can integrate with Azure Data Lake Storage
Enables storing and analyzing streaming data streams at scale
Integration with Power BI:
Power BI is a business intelligence and analytics platform
Provides interactive visualizations and reports
Azure Synapse Real-Time Analytics can integrate with Power BI
Visualizes and explores streaming data in real-time
Enables users to gain insights and make data-driven decisions
Azure Data Explorer (Formerly Kusto DB):
Azure Kusto DB, also known as Azure Data Explorer, is a fast, fully managed data analytics service by Microsoft Azure
Designed for analyzing large volumes of structured, semi-structured, and unstructured data in real-time
Optimized for ad-hoc queries, interactive analytics, and time-series analysis
Well-suited for use cases such as log and telemetry analytics, IoT data analysis, and application performance monitoring
Key features of Azure Kusto DB include:
Scalability:
Built on a distributed, columnar storage architecture
Scales horizontally to handle massive volumes of data
Capable of ingesting and analyzing petabytes of data in real-time
Enables organizations to gain insights from large datasets quickly and efficiently
Query Language:
Kusto Query Language (KQL) is a powerful and intuitive query language
Used for querying and analyzing data in Kusto DB
Supports a wide range of data manipulation and analysis functions
Includes filtering, aggregating, joining, and visualizing data
Time-Series Analysis:
Optimized for time-series analysis
Ideal for analyzing data with timestamped events or time-based metrics
Provides built-in support for time-based queries
Offers windowing functions for advanced analysis
Includes advanced time-series analysis techniques
Integration with Azure Services:
Kusto DB integrates seamlessly with Azure Monitor, Azure IoT Hub, Azure Data Factory, and Azure Stream Analytics
Enables ingestion of data from various sources into Kusto DB for analysis and visualization
Users can leverage additional Azure capabilities for data processing and analytics
Includes robust security features and compliance controls
Protects sensitive data and ensures regulatory compliance
Supports encryption, access controls, auditing, and compliance certifications
Cost-Effective:
Kusto DB offers a consumption-based pricing model
Users pay only for the resources they consume
Cost-effective for analyzing large volumes of data
Eliminates worries about upfront infrastructure costs or over-provisioning
Commonly used for real-time analytics, log and telemetry analytics, monitoring and diagnostics, IoT data analysis, and application performance monitoring
Provides a powerful and flexible platform for gaining insights from large datasets
Drives data-driven decision-making across the organization
Power BI Copilot:
Copilot in Power BI integrates generative AI functionalities
Accelerates exploration and dissemination of insights from large datasets
Users can articulate specific insights or interrogate data
Tool rapidly analyzes and extracts pertinent information
Generates visually captivating reports
Translates data into actionable insights in real-time
Streamlines decision-making and knowledge-sharing workflows
Users articulate desired visualizations and insights, leaving the tool to handle the remainder
Data Activator:
Data Activator is a monitoring tool in Azure Fabric
It monitors in real-time
It can detect alert condition and trigger an event such as alerting through email or kicking off a workflow
Its configuration does not require any coding
It can be configured to take actions when patterns or conditions are detected in changing data.
It is preview mode currently
Components of automated triggers
Event:
Ass data sources are treated as streams of events.
An event is an observation about the state of an object, with some identifier for the object itself, a timestamp, and the values for fields you’re monitoring
It also integrates with Power BI for identifying real-time events
Business Objects
Business objects are data related to physical entities that are monitored.
Example: a package, inventory level, temperature in a temperature sensitive zone
Triggers
Triggers are conditions that are being monitored on a business object
Properties
These are configuration properties for a condition being monitored and action to be taken