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Microsoft Fabric is Microsoft’s data integration and analytics platform. It brings together data engineering, data management, data analysis, and business intelligence functions. Microsoft Fabric aims to make data operations more efficient and scalable by providing all these functions on a single integrated platform. In today’s data-driven world, businesses have recognized the need to manage data on secured platforms that provide a single source. In this context, Microsoft introduced Fabric in November 2023, a product that encompasses everything from data transport to data science, real-time analysis, and business intelligence. This platform, aiming for simplicity, consolidates its services on Azure under a Software as a Service (SaaS) model. Consequently, data engineers, analysts, or scientists can now collaborate more effectively.

Microsoft Fabric Features

Cloud-Based Infrastructure

Cloud computing is the delivery of computing services (including servers, storage, databases, networking, software, analytics, and artificial intelligence) over the Internet (‘the cloud’) to provide faster innovation, flexible resources, and economies of scale. Cloud infrastructure consists of the hardware and software components used to deliver cloud computing services.

Data Management and Integration

Data management encompasses the processes, principles, roles, measurements, and standards that enable the effective and efficient use of information. Data integration is the process of combining data from different sources to provide users with a single, unified view.

High Performance and Scalability

Performance refers to the efficiency of a system under a specific workload over a given time. Scalability involves parameters that we can adjust to enhance performance when needed. In the cloud, we validate performance by testing scalability. Examples of items you should measure include:

Resource Utilization:

– CPU Load
– Memory Usage
– Disk I/O
– Read/Write Database Queries

Application Statistics:

– Number of Requests
– Response Time

Security and Compliance

To identify and protect against rapidly evolving threats, it is essential to implement multi-layered built-in security measures, avoid misconfigurations, and establish safeguards in your resources to ensure consistent resource management.

AI and Machine Learning Support

The Synapse Data Science service in Microsoft Fabric provides a Software as a Service (SaaS) experience that assists machine learning experts in building, deploying, and preparing machine learning and artificial intelligence models on a single analytics platform, while also collaborating with other key roles.

One of the key features Microsoft implements with Fabric is Microsoft Copilot for data insights. This AI-powered assistant can answer questions related to data. Since it is connected to the broader Fabric ecosystem, you can apply it to data across your organization.

Comprehensive Analytics and Reporting Tools

Data scientists have access to powerful tools and services for complex data analysis and processing, such as Apache Spark integration. Microsoft’s flagship business analytics tool, Power BI, is set to leverage numerous advantages from Fabric, including:

Enhanced Data Access: Fabric ensures seamless data ingestion by allowing Power BI to pull data from a wider range of sources. Direct connections from Power BI to Delta tables in the data lake eliminate the need for maintaining a SQL layer, resulting in significant cost savings.

Real-Time Analysis: Streamlined data processing enables lower-latency analyses, facilitating quicker decision-making.

Unified Reporting: As Fabric breaks down data warehouses, reports can pull data from multiple Azure services, leading to more comprehensive reporting.

User Experience and Interface Features

Power BI and other integrated tools provide intuitive interfaces that allow non-technical users to explore data and create basic reports.

Microsoft Fabric Components

Microsoft Fabric offers a comprehensive suite of analytical experiences designed to work seamlessly together. The platform tailors each of these experiences to specific personas and tasks.

Fabric hosts three main data analysis products by Microsoft: Power BI, Azure Data Factory, and Azure Synapse (Figure 1).

Figure 1. Microsoft Fabric Components

Data Engineering:

Microsoft Fabric provides data engineers with a specialized infrastructure and system set designed to handle large volumes of data, enabling users to effectively manage them.

Data Science:

Microsoft Fabric offers a comprehensive data science experience that allows users to successfully execute end-to-end data science workflows for data enrichment and gaining business insights. Users can successfully complete a wide range of activities throughout the data science process, from data discovery, preparation, and cleansing to experimentation, modeling, model scoring, and prediction. This process extends to integrating the insights gained into BI reports.

Data Warehouse:

Users can create virtual data warehouses to query desired data across databases. These warehouses can source data from OneLake, Azure Data Lake Storage, or other cloud vendor storages without duplicated data within a single boundary. Queries can be performed using SQL technology combined with Apache Spark for handling big data. Alternatively, users can utilize the Visual Query editor, which provides a code-free alternative to SQL queries (Figure 2). Additionally, these systems can scale up as usage demands increase or scale down when not needed, without requiring user intervention.

Figure 2. SQL Queries

Data Factory:

Data Factory combines the best features of Azure Data Factory and Microsoft Power Query. Known as the Azure version of SSIS (SQL Server Integration Services), Data Factory enables data movement and transformation, allowing for complex data integrations and ETL scenarios. Here, activities such as data transformation using dataflow and data copying via data pipeline can be managed, supporting one or multiple activities.

Figure 3. Data Pipeline

Real-Time Analytics:

Applications we use, IoT devices, and high-level operational systems result in large-scale data. Systems that move this data’s ETL to data warehouses and run ETL processes at specified times may not suffice. Therefore, data flow systems are created to collect changes occurring in source systems through mechanisms like event processing and APIs. Microsoft Fabric features real-time analytics for data flow. On this platform, data is gathered from multiple sources using Eventstream and directed to relevant destinations (Lakehouse, KQL Database, Custom App). Queries can be prepared, saved, and shared using the KQL Query Set. Lastly, real-time reports and dashboards can be created using Power BI.

Advantages of Microsoft Fabric

Microsoft Fabric is a versatile platform that offers a wide range of benefits for businesses and individuals. With a focus on seamless integration and scalable solutions, Microsoft Fabric has become an indispensable tool for those looking to enhance productivity, streamline operations, and improve overall digital experiences.

Flexibility and Customization Options

The flexibility of Microsoft Fabric is a significant advantage. It caters to a wide variety of workloads and applications, making it suitable for various industries and purposes. Whether you need to host a website, run a data analytics project, or deploy a mobile application, Microsoft Fabric provides the tools and resources to meet your specific needs. This adaptability is vital in a fast-paced, ever-changing digital environment.

Enhanced Collaboration Capabilities

Microsoft Fabric includes features that support collaboration and communication within your organization. With tools like Microsoft Teams and SharePoint, your teams can collaborate more effectively regardless of their physical locations. Real-time document sharing and video conferencing capabilities enhance productivity and create a collaborative working environment.

Cost Optimization and Return on Investment

Microsoft Fabric helps you optimize your spending by eliminating the need for a significant upfront investment in hardware and software, significantly reducing your capital expenditures. Moreover, the pay-as-you-go pricing model allows you to pay only for the resources you use, resulting in substantial long-term savings. This makes Microsoft Fabric a cost-effective solution for organizations of all sizes.

Comprehensive Data Security

Microsoft Fabric provides robust security measures to protect your data and applications. It includes features aimed at safeguarding sensitive information, such as identity and access management, data encryption, threat detection, and compliance management. With Microsoft Fabric, you can have peace of mind knowing your digital assets are well-protected.

Rapid and Easy Deployment

Whether it’s Microsoft Azure, Office 365, or any other Microsoft tool, Fabric ensures that your digital ecosystem functions as a cohesive unit, saving time and reducing operational friction.

Integration and Compatibility

One of the standout features of Microsoft Fabric is its ability to integrate seamlessly with other Microsoft products and services. This integration simplifies workflows by allowing data and applications to work together effortlessly. Whether it’s Microsoft Azure, Office 365, or any other Microsoft tool, Fabric ensures that your digital ecosystem operates as a unified unit, saving time and minimizing operational friction.

Industry Applications of Microsoft Fabric

Microsoft Fabric in Healthcare

The healthcare data solutions in Microsoft Fabric provide an end-to-end analytics SaaS platform that allows you to ingest, store, and analyze health data from various sources, such as electronic health records, imaging archiving, and communication systems. These solutions offer data models and transformation activities that help customers build multimodal repositories. With the healthcare data solutions in Microsoft Fabric, you can access and query your health data using familiar experiences like Azure Synapse Analytics, Azure Data Factory, and Microsoft Power BI.

 Microsoft Fabric in Finance and Banking

Microsoft Fabric is not a finance-focused product. Instead, it is a platform that enables individuals across various departments, including finance and accounting, to benefit from robust data and analytics capabilities.

Frequently Asked Questions about Microsoft Fabric

What products can I create user groups for?

User groups can be created for Power BI, Synapse, and Data Factory.

What is the migration path for existing customers using Data Factory and Synapse?

Existing customers using Data Factory and Synapse will continue to receive support, and there is no urgent need for migration. The Data Factory team is developing migration tools for customers wishing to transition to Fabric, ensuring a smooth transition. While the team is committed to ongoing investment in Data Factory and Synapse, future feature updates may focus on Fabric.

What is the difference between the data factory in Fabric and the data engineering tab?

Data Factory helps you solve complex data integration and ETL scenarios with cloud-scale data movement and transformation services, while data engineering helps you build a data lake and use Apache Spark to transform and prepare your data. Differences between the terminologies/experiences in Fabric are available under Microsoft Fabric terminology.

What is the difference between Fabric and Synapse?

The main difference lies in data storage. Unlike Azure Synapse Analytics, Microsoft Fabric does not have a dedicated SQL pool or relational storage. Instead, the repository data is persisted in delta lake format within OneLake. Additionally, since Fabric is a SaaS, you do not need to create and manage Apache Spark pools.

What are the differences between Microsoft Fabric and other Microsoft solutions?

In recent years, Microsoft has launched a series of products and features that fundamentally change the data and analytics landscape. The latest iteration in this series is Microsoft Fabric. Microsoft describes Fabric as a “unified analytics platform.” So, what does this really mean?

In many ways, Microsoft Fabric can be seen as a rebranding and repositioning of its existing services, but with added features and functionalities. This represents a new and unified approach to how we process our data. To better understand Fabric’s role within the ecosystem, Microsoft depicts the following diagram as a new and refreshed umbrella encompassing many tools you may already be using.

What are the main advantages of Microsoft Fabric?

– Reduces the need for data copying for different OneLake solutions.
– Allows company users to focus on complex objectives, such as machine learning or data science projects.
– Reduces the complexity of pricing models by introducing universal computing capabilities.
– Includes data analysis platforms like Azure Data Factory, Data Explorer, and Apache Spark, integrated as an additional package within Microsoft Fabric.
– Utilizes open-source document formats and Delta Lake.
– Simplifies model architecture and enables immediate, actionable insights.

What technical skills do I need to use Microsoft Fabric?

If you are familiar with tools like Power BI, you won’t need to adapt most of your approaches to use Microsoft Fabric. Remember, this is a unified platform aimed at breaking down walls between business users. Most companies do not deploy Microsoft Fabric directly; it is typically managed by IT teams with existing Microsoft setups.

Is Microsoft Fabric cloud-based, or can it be used on-premises as well?

Microsoft Fabric is used as SaaS on Azure, so it cannot be used on-premises.

How is Microsoft Fabric set up, and what are the first steps?

The entire interface/access of Microsoft Fabric operates through the Power BI service. So, it’s a familiar environment! A user with the Power BI Admin, Power Platform Admin, or Global Admin role can activate Fabric for your entire tenant or for a specific user group. If you have a Power BI Premium license, you can also activate Fabric for the capacity you purchased.

You can activate Fabric from the tenant settings on the Power BI Admin page.

Microsoft 365 Developer Account: This license provides you access to a Power BI Pro account.

Log in to Microsoft Power BI with your Developer Account.

Activate your Microsoft Fabric tenant.

Which software can be integrated with Microsoft Fabric?

The main components that can be integrated with Microsoft Fabric include:

– Power BI
– Data Factory
– Data Activator
– Industry Solutions: Manufacturing data solutions in Microsoft Fabric (preview) and copilot template for factory operations on Azure AI, retail data solutions in Microsoft Fabric, sustainability data solutions in Microsoft Fabric.
– Real-Time Intelligence
– Synapse Data Science
– Synapse Data Warehouse

What are the costs associated with Microsoft Fabric?

Power BI Pro or PPU licenses are not sufficient for Fabric. If you hold a Premium license, you can add Fabric features to the capacity you have purchased.

Sources: 

Source: Erwin de Kreuk Blog – Real-Time Analytics in Microsoft Fabric

Source: Atlan – What is Microsoft Fabric

Source: İlhami Demirci (Medium) – Microsoft Fabric 101

Source: Microsoft Learn – Microsoft Fabric Overview

Source: Enterprise DNA Blog – What is Microsoft Fabric

 
Prepared by: GTech DWH & BI Team

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In Which Sectors Is Business Intelligence Used?

Business intelligence can be used easily and beneficially in any sector where data can be accessed. Today, business intelligence is frequently and efficiently used in areas such as banking, insurance, telecommunications, retail and public services; It is used as an important resource in the decision stages of companies.

What are the Components of Business Intelligence?

Business intelligence has 5 basic components;

• Data collection and storage
• Creating a data warehouse
• Creating a data model
• Data analysis
• Making decisions based on analysis results

Is Business Intelligence Necessary?

Nowadays, the entire world has become one market, and a development occurring in one place is affecting the rest of the world very quickly. This situation has also changed the conditions and opportunities of competition, and has brought companies into competition on a global scale. It is now known by all companies that information is much more valuable, and can be processed much faster than in the past, and efforts are made to make the most of this situation. Business intelligence comes into play at this point; It enables the processing and analysis of the obtained big data and facilitates the decision-making of companies. Correctly designed business intelligence applications provide significant benefits in determining the targets of enterprises, in making risk assessments and in reducing their costs.

Business Intelligence Decision Support Systems

There are 3 different decision types in companies: structured, semi-structured and unstructured. Among these, the decisions defined as structured are those used especially for operational activities and the method to be followed is determined in advance. Semi-structured decisions are more flexible and open to change than structured decisions. Unstructured decisions are usually strategic-level decisions and it is not always possible to make such decisions in advance and to determine the path to follow.

Business intelligence decision support systems are used in semi-structured or unstructured decision-making processes. Business intelligence decision support systems; It facilitates decision-making by using databases, analytical models, decision maker’s opinions and computer-based modeling processes. Customizable business intelligence decision support systems can also provide the ability to analyze the decision maker’s previous decisions and act in accordance with these decisions.

GTech enables you to make strategic decisions by analyzing your company data. Data Warehouse and Business Intelligence provides end-to-end service in the implementation of solutions and data-oriented modern business intelligence decision support systems.

For more detailed information about GTech’s products and services, please contact us.

Prepared by: GTech DWH & BI Team

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How is Data Collected?

There are 2 main ways to collect data. The first of these ways is the “Open data collection” method, which is performed with your permission and based on the information you have entered or your behavior within the site. The other method is called “Closed data collection” and it is usually the data collected on sites such as social networks and search engines. The purpose of this type of data collection method is to follow all behaviors on that site, determine the interests of the user and carry out marketing activities accordingly.

What Are Data Mining Processes?

• Data Filtering: It is the stage of determining the data to be used in mining.
• Data Cleaning: It is the step of removing unnecessary, inconsistent or noisy ones from the collected data.
• Data Merging: Data obtained from different sources and with similar characteristics or related data are combined in this step.
• Data Selection: The process of selecting the cleaned and merged data suitable for analysis is carried out in this step.
• Data Conversion: It is the stage of converting the available data into a form suitable for mining.
• Mining Study: At this stage, appropriate data mining algorithms are applied on previously prepared data.
• Interpretation and Verification: After the data mining application is carried out, the results obtained are interpreted and research is carried out on the accuracy of these results. The verification process is carried out by comparing the results obtained from different applications.

What Are Data Mining Methods?

• Classification: One of the most widely used methods in data mining is the classification method. This method is to examine the properties of the available data and transfer it to the appropriate one among the previously determined classes.
• Association Rules: This is another method commonly used in data mining. This method aims to identify the interconnected data contained in large databases and the connections between them.
• Clustering: The purpose of this data mining method is to separate the data into subclasses according to the relationships among themselves.
• Estimation: It is a data mining method based on the estimation of numerical data that is missing in a data set.
• Contradiction Analysis: It is the method of detecting excessive deviations in the data. Unusual expenditures from credit cards are detected with this method.

In Which Areas Is Data Mining Used?

There is no limitation regarding the usage areas of data mining, and data mining can be performed wherever data is accumulated. If we look at the areas where data mining is used most widely today;

• Banking
  • Determining customers according to their credit card usage habits.
  • Making evaluations on loan requests.
• Marketing
  • Determining the purchasing habits of individuals.
  • Sales Estimation
  • Market Basket Analysis
• CRM:
  • Increasing customer loyalty.
  • Making efforts to make the most of marketing campaigns.
• E-Trade:
  • Detecting attacks on servers.
  • Determining the behavior of users browsing the website.
• Insurance Business
  • Determination of insurance risk groups.

GTech provides end-to-end service in the implementation of Data Warehouse and Business Intelligence solutions that will enable organizations to make strategic decisions by analyzing their data, and in the implementation of modern data-driven decision support systems.

Prepared by: GTech DWH & BI Team

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What Are Business Intelligence Tools?

Business intelligence tools; It is application software that processes large amounts of data collected from customer records, user records, images, files, e-mail, video and business activities as well as many other sources.

These application software provide querying and reporting of business data based on simple rules. It can use different applications such as quick query and analysis, reporting, real-time business intelligence, operational business intelligence, mobile business intelligence, cloud-based business intelligence and open source business intelligence.

What is in the Scope of Business Intelligence?

Business intelligence consists of many interrelated and interacting components. The prominent among these components are;

• Data mining
• Process management techniques (also called process mining)
• Multidimensional analysis processes
• Prediction or rule-based logical analysis
• Comparison of the business process with other business process examples using various performance measurement tools
• Reporting

What are the Advantages of Business Intelligence?

• Provides Productivity Increase: Business intelligence, which enables detailed reports to be created based on very large amounts of data in a very short time, provides important information to businesses and helps to use resources more effectively.
• Provides Transparency: Business intelligence, which uses very small or overlooked details in analysis, increases the visibility of business processes and enables problems to be detected more easily.
• Simplifies Business Processes: Benchmarking, forecasting, modeling and other business intelligence methods remove the complexity of business processes and make them more understandable.
• Ensures Understandability of Data: It reduces dependency by enabling technical data to be created, analyzed and interpreted even by non-technical experts.

What Are Business Intelligence Methods?

Business intelligence has many methods that businesses can use in their decision and implementation processes. The prominent among these methods are;

• Analytical: Provides meaningful insight and trends by working on raw data.
• Predictive Modeling: Predictive modeling used by business intelligence, enables seeing probabilities and trends through models created using various statistical techniques.
• Data Visualization: The data visualization method, which is realized by revealing the data visually, allows insights that cannot be expressed or made sense with numbers or letters.

Why GTech in Business Intelligence Applications?

GTech specializes in Data Warehouse and Business Intelligence and offers solutions to create visual, fast and interactive reports that will enable customers to analyze their data and make strategic decisions whenever they need it.

With GTech’s business intelligence solutions, businesses;

• Gains a single and reliable data source. In this way, it provides relational access to the scattered data stored in different systems, in a single environment and securely.
• By providing fast access to the information obtained from the data, it allows the time and costs spent for reporting and subsequent decision-making processes to decrease.
• Increases observability by performing customer and segmentation analyzes along with the scorecard and performance indicators.
• It enables the display of many analyzes made for different purposes or using different methods as a whole on summary screens and enables dynamic analysis.
• It ensures the integration of data from different platforms on a single platform and the use of the most accurate data in decision-making processes.
• Allows your business to make the right decisions and develop strategies.

Contact us to immediately meet with our Data Warehouse and Business Intelligence solutions.

Prepared by: GTech DWH & BI Team

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