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Microsoft Azure Service Fabric is an AI-powered data analytics platform offering seamless integration and collaborative insights. The platform enables data analytics for the entire organization by providing a centralized, single source of truth, and the Copilot feature makes it easier to uncover new insights. Streamlining data analytics services, it enhances efficiency and productivity. Moreover, the platform offers transparent and flexible billing, empowering organizations to make data-driven decisions and drive innovation effortlessly. In essence, Microsoft Fabric serves as a powerful catalyst in transforming data into a strategic asset.
### **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, creating a seamless environment for analytics workflows. Built on a foundation of Software as a Service (SaaS), Microsoft Fabric delivers simplicity and enhanced integration capabilities. By bringing together components from Power BI, Azure Synapse, and Azure Data Explorer, Microsoft Fabric offers a cohesive and integrated environment that 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 the entire suite.
### **Components of Microsoft Fabric**
At the core of Microsoft Fabric lies Microsoft Fabric Lake, a powerful solution built on ADLS (Azure Data Lake Storage) Gen2. It offers a seamless SaaS experience, serving as a unified storage system for all data consumers. With centralized enforcement of policy and security settings, Microsoft Fabric ensures compliance and uniformity across the board. OneLake provides a robust foundation for managing and accessing data, enabling organization s to streamline operations and achieve optimal data governance.
Microsoft Fabric brings together a comprehensive suite of services including Data Factory, Synapse Data Engineering, Synapse Data Science, Synapse Data Warehousing, Synapse Real-time Analytics, Data Activator, and Power BI Copilot. With Fabric, organizations gain the flexibility of serverless processing, enabling efficient execution of SQL, Spark, and KSQL queries.
### **Data Factory**
The Fabric Data Factory serves as a versatile tool for ingesting, preparing, and transforming data from a wide range of sources, including real-time streams, databases, data warehouses, structured, unstructured, and semi-structured data, as well as data lakes. With an extensive set of approximately 300 transformation features, including AI-based capabilities, the Data Factory designer enables users to perform advanced data manipulations. Additionally, Data Factory offers out-of-the-box, robust data orchestration capabilities, allowing users to effortlessly compose flexible data workflows that meet their specific needs.
### **Synapse Data Engineering**
Synapse Data Engineering empowers users to architect, construct, and uphold infrastructures and systems that enable organizations to efficiently gather, store, process, and analyze vast amounts of data. From the Data Engineering homepage in Microsoft Fabric users can:
* Design and implement data pipelines
* Use Spark Job definitions to submit batch and streaming jobs to Spark cluster
* Ingest and manage data using Lakehouse
* Use notebooks to write codes for data transformation
* Import notebooks and collaborate
### **Synapse Data Science**
The Synapse Data Science tool empowers data scientists to engage in a diverse range of activities throughout the entire data science process. From data exploration, preparation, and cleansing, to experimentation, modeling, and the scoring and serving of predictive insights to BI reports, Synapse Data Science provides a comprehensive platform for end-to-end data science workflows. With Synapse, data scientists can seamlessly navigate through each stage of the process, leveraging powerful tools and capabilities to derive valuable insights and drive informed decision-making.
Users can create POCs and models, and implement the solutions using Synapse Data Science Home page. They can import an existing notebook and further enhance existing capabilities. Example use cases include fraud detection, customer churn, and ML notebooks.
### **Synapse Data Warehousing**
Microsoft Fabric offers two types of data warehouses: a read-only SQL Endpoint and a traditional Synapse Data Warehouse. The SQL Endpoint is generated automatically upon creation from a Lakehouse in Microsoft Fabric. It serves as a read-only warehouse where Delta tables created through Spark in a Lakehouse are seamlessly discoverable as tables in the SQL Endpoint. On the other hand, the Synapse Data Warehouse, also known as Warehouse, is a “traditional” data warehouse that supports full transactional T-SQL capabilities, akin to an enterprise data warehouse. Unlike the SQL Endpoint, tables and data need to be created manually in the Synapse Data Warehouse. Microsoft Fabric provides both options to cater to different data warehousing needs and preferences.
### **Synapse Real-time Analytics**
Synapse Real-Time Analytics is a comprehensive and managed big data analytics platform specifically designed for streaming and time-series data. With its seamless integration into the entire suite of Fabric products, it facilitates effortless data loading, transformation, and advanced visualization scenarios. Synapse Real-Time Analytics effortlessly processes structured, semi-structured, and unstructured data, offering flexibility and versatility. The key components of the Synapse Real-Time Analytics tool include:
1. Eventstream, a user-friendly, no-code feature for capturing, transforming, and routing real-time events.
2. KQL database, which serves as a reliable storage and management system for data.
3. KQL queryset, enabling query execution, saving and exporting result sets for further analysis, and reporting.
### **Power BI Copilot**
Copilot in Power BI merges advanced generative AI capabilities with data to expedite the process of discovering and sharing insights. Copilot allows users to effortlessly describe the specific insights they require or inquire about their data, and the tool will swiftly analyze and extract the relevant data, ultimately presenting it in a visually striking report. By seamlessly transforming data into actionable insights in real-time, Copilot empowers users to expedite their decision-making and knowledge-sharing processes. With Copilot, simply articulate the desired visuals and insights, and the tool takes care of the rest.
### **Data Activator**
Data Activator allows users to monitor data activities in Microsoft Fabric in real-time. Easily configure evaluation of different conditions to determine alert conditions that automatically trigger responses across a variety of systems. Data Activator can be configured to prompt actions, such as sending an email or running a Power Automate workflow.
### **What sets Microsoft Fabric apart?**
* Microsoft Fabric is a Software as a Service (SaaS) offering from Microsoft that provides an all-in-one analytics solution for enterprises covering everything from data movement to data science, Real-Time Analytics, and business intelligence. Fabric is powered by Azure OpenAI Service to help customers unlock the full potential of data. Data Consumers can leverage the power of generative AI to generate insights from data.
* With Copilot in Microsoft Fabric, users can use conversational language to create dataflows and data pipelines, generate code and entire functions, build machine learning models, and visualize results.
* Fabric enables data democratization by integrating Microsoft 365 applications that users use every day.
* Fabric reduces cost through unified capacities.
### **Pricing**
Microsoft Fabric has an array of capacities available for purchase. The capacities are split into Stock Keeping Units (SKU), with each providing a different amount of computing power, measured by its Capacity Unit (CU) value.
Microsoft Fabric operates on two types of SKUs:
* Azure – Billed per second with no commitment.
* Microsoft 365 – Billed monthly or yearly, with a monthly commitment.
Microsoft recommends using Azure SKU for using Microsoft Fabric capacity, which is based on the pay-as-you-go model with no commitments. Capacity can be paused and resumed as needed.
### **Why proSkale**
At proSkale, we stay current with the latest data and cloud analytics solutions. As a Microsoft technology partner with a proven track record of developing numerous accelerators, we are well-equipped to assist you on your journey towards rapid cloud adoption. Microsoft Fabric is still in preview phase, but our engineers have tried it and believe that this AI-powered data storage and analytics solution will be a game changer. Stay tuned to our future articles on this topic and contact us to learn more about our cloud, data, and analytics capabilities!
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In today’s data-driven world, organizations are increasingly relying on data analytics to gain valuable insights and make informed decisions. Data Lakehouse, a powerful data storage and analytics framework, offers a scalable and cost-effective approach to manage and analyze vast amounts of data. In this blog post, we will explore the key features and benefits of a Data Lakehouse and how it empowers organizations to maximize their data analytics potential.
### **What Is A Data Lakehouse**
A Data Lakehouse is a modern data architecture that addresses some limitations and challenges associated with traditional Data Lakes and Data Warehouses. It combines the advantages of each, aiming to provide a unified and scalable platform for storing, processing, and analyzing data, offering the best of both worlds to end users.
Both Data Lakes and Data Warehouses serve specific purposes and cannot alone handle the complexities of modern data environments.
A Data Lake is a storage repository that can handle large volumes of structured, semi-structured, and unstructured data. Data is stored in its raw and original format, without a predefined schema.
A Data Warehouse is a data storage and processing system that specializes in storing structured data. It utilizes a predefined schema and is highly optimized for efficient querying and analytics processes.
In a Data Lakehouse architecture, the Data Lake serves as the central repository for storing large volumes of raw data. The data is then transformed, curated, and organized into a structured format within the Data Lakehouse.
Once the data is organized and structured, it can be accessed by analytics tools and platforms for querying, reporting, and machine learning.
The Data Lakehouse architecture enables both batch and real-time processing, combining the scalability and cost-effectiveness of a Data Lake with the performance optimizations and query capabilities of a Data Warehouse.
**Figure 1**: Lakehouse architecture (@proskale.com)
### **Features of a Data Lakehouse**
* Unified data storage
* Schema enforcement
* Data engineering and data science
* Time travel analysis
* Real-time insights
* IoT data processing
* Streaming data processing
* Concurrent read and write (ACID compatibility of transactions)
* Customer 360
* Scalability and performance
* Cost effectiveness
* Integration with multiple cloud services
* Security
* Open and standardized storage formats
* Separation of storage and processing
* Advanced analytics
### **Concluding Remarks**
To summarize, a Data Lakehouse presents a robust and compelling solution that harnesses the strengths of both Data Lakes and Data Warehouses. It seamlessly integrates storage, processing, and analytics capabilities, enabling organizations to achieve smooth data integration, advanced analytics, and real-time insights. With its versatility in handling structured and unstructured data, Data Lakehouse empowers organizations to dismantle data silos and democratize data access, fostering a culture of collaboration and data-driven decision-making.
As organizations continue to realize the significance of their data assets, embracing a Data Lakehouse architecture becomes an essential strategic move. By adopting the Data Lakehouse, organizations embark on a transformative journey that unlocks the true potential of their data. This enables them to make well-informed decisions, foster innovation, and gain a competitive advantage in the digital era.
proSkale has developed several accelerators for building and managing cloud-based solutions, including Data Lakehouse for three major cloud service providers – Amazon AWS, Microsoft Azure, and Google Cloud. All three provide a framework for Data Lakehouse and we will explore each further in future posts. Our large pool of skilled and trained cloud data engineers and analysts have helped many of our clients progress their digital transformation journeys and, using proSkale’s cloud and data analytics solutions, have stood up a Data Lakehouse architecture within a matter of weeks. Contact us today to discuss how we can rapidly help your modernize your data and supporting applications.
### **References:**
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