Microsoft Fabric

Modern organizations use many systems that store and process data. Some of them operate in on-premises environments—for example ERP systems, SQL Server databases, or industrial solutions. At the same time, an increasing amount of data is generated in cloud services and SaaS applications..

Such an environment leads to the creation of distributed data sets. Information resides in different systems, is stored in various formats, and often requires additional processing before it can be used for analysis or reporting.

One of the main challenges, therefore, is designing an architecture that enables secure and efficient integration of data from multiple sources—both on-premises and cloud-based.

Microsoft Fabric was designed precisely for this scenario. The platform enables the creation of a unified analytics environment in which data from different systems can be integrated, processed, and shared within a single data model.

Microsoft Fabric is an environment designed for processing, analyzing, and sharing data within a single platform. It combines tools used at different stages of working with data – from integration and processing, through modeling, to analysis and visualization.

In traditional architectures, individual elements of the data ecosystem – such as data integration tools, data warehouses, or reporting solutions – often operate as separate systems. In Microsoft Fabric, they are integrated within a single platform built on a shared data layer.

The base of the entire platform is OneLake, a shared data storage layer. It allows different components of Microsoft Fabric to work on the same datasets without the need to copy data between systems.

This approach simplifies the architecture of analytics environments and enables the creation of unified data platforms that include both on-premises and cloud data sources.

In many organizations, data is stored both in on-premises systems and in cloud services. These can include SQL Server databases, ERP systems, industrial solutions, or cloud-based business applications.

Microsoft Fabric allows these data sources to be integrated within a single architecture. The platform provides an environment where data from different systems can be processed, stored, and analyzed in a consistent manner.

In the Microsoft Fabric architecture, components responsible for data processing and the data storage layer play a key role. Individual elements of the platform – such as the lakehouse, data warehouses, and analytics tools – use a shared data environment and can work on the same datasets.

This enables the creation of analytical solutions that encompass both on-premises and cloud data without the need to build complex, multi-layered integrations between systems.

One of the key elements of the Microsoft Fabric architecture is OneLake. It is a shared data storage space that serves as the central repository for the entire analytics platform.

In traditional data architectures, different tools often use separate data stores or require copying data between systems. OneLake takes a different approach – data is stored in a single environment and can be accessed by various platform components.

This allows tools such as the lakehouse, data warehouses, and Power BI to use the same datasets without the need for repeated processing or moving data between services.

An important feature of OneLake is shortcuts, which enable logical sharing of data stored in other locations. This allows data from different systems to be used without physically copying it to a single location.

This approach simplifies data management and enables the creation of unified analytics environments that encompass multiple data sources.

In many organizations, critical business data still resides in on-premises systems. These can include SQL Server databases, ERP systems, industrial solutions, or other applications running within the organization’s infrastructure.

Microsoft Fabric enables the integration of such data sources with the cloud-based analytics platform. This allows locally stored data to be used in analyses, reports, and data processing workflows executed within the Fabric environment.

The platform provides several mechanisms to facilitate this integration, including:

On-Premises Data Gateway

On-Premises Data Gateway enables a secure connection between on-premises environments and Microsoft Fabric cloud services. This component allows access to data stored in local databases without directly exposing it to the internet.

The gateway acts as a communication intermediary between cloud services and on-premises systems, ensuring access control and connection security.

Data Mirroring

The mirroring mechanism allows data from selected source systems to be synchronized into the Microsoft Fabric environment. This creates copies of data within the analytics platform that can be used for analysis and reporting.

Mirroring helps maintain up-to-date data in the analytics environment without manually transferring information between systems.

Data Agent

The Data Agent automates the integration of on-premises data with Microsoft Fabric. It enables data retrieval from selected sources and further processing within the analytics environment.

With these mechanisms, Microsoft Fabric can serve as a central data platform, unifying information from both on-premises infrastructure and cloud services.

Once data from various sources is integrated, the next step is preparing it for analysis. In Microsoft Fabric, tasks related to data integration and processing are carried out using tools available in Data Factory.

Data Factory enables the creation of data processing workflows that cover different stages of working with information – from extracting data from source systems, through transformation, to loading it into target data structures.

In practice, this allows building ETL (Extract, Transform, Load) or ELT processes, where data is processed and prepared for further use in analyses and reports.

Microsoft Fabric also supports the automation of these processes. Data flows can be scheduled to run periodically or triggered by specific events, ensuring that data in the analytics environment remains up to date.

This approach allows data from different systems to be processed consistently and made available within a unified analytics architecture.

In practice, an organization’s data architecture often includes multiple information sources. These can be databases operating on-premises, business systems such as ERP or CRM, as well as applications running in cloud services.

Microsoft Fabric enables the creation of an environment where data from these systems can be integrated and processed within a single analytical model. Data can be extracted from various sources, processed through integration workflows, and then used for analysis and reporting.

(In the diagram: data flows between on-premises systems, OneLake, lakehouse, warehouse, and Power BI)

In this model, data from multiple systems is consolidated into a shared data environment, where it can be further processed and analyzed. Individual components of the Microsoft Fabric platform—such as the lakehouse, data warehouses, and analytics tools—can use the same datasets.

This approach simplifies the architecture of analytics systems and allows organizations to build solutions based on a consistent data model.

In many IT environments, data is not stored in a single location. Some systems operate within the organization’s on-premises infrastructure, while other applications run in cloud services. This model is common, especially in companies that are gradually expanding their data environments or using multiple business systems.

A hybrid architecture enables the integration of these environments within a single analytics platform. This allows data from different systems to be processed and analyzed consistently.

Microsoft Fabric supports the construction of such architectures by integrating data from multiple sources and processing it within a single analytics platform. This allows organizations to expand their data environments without the need to move all systems into one environment.

Building an analytics environment that integrates data from various systems involves specific challenges. These include how data is processed, how its structure is managed, and how an appropriate level of security is maintained.

A key element is the proper design of data integration processes. In environments with multiple information sources, it is important to define how data is extracted, processed, and stored to ensure consistency and currency.

Access management also plays a critical role. In environments spanning multiple systems, it is necessary to control which teams and users can access specific datasets and how they are allowed to process that data.

Monitoring data processing workflows is another important aspect. In analytics environments based on multiple sources, tracking integration processes and identifying potential issues in data processing is essential.

Applying proper design practices and leveraging the tools available in Microsoft Fabric enables the creation of data environments that are scalable, secure, and tailored to the analytical needs of the organization.

In many organizations, data is stored across different systems—both on-premises and in cloud services. Integrating these environments is one of the main challenges of modern data architecture.

Microsoft Fabric is designed as a platform that enables the connection of these areas within a single analytics architecture. Through a shared data layer, integration mechanisms, and tools for data processing and analysis, it is possible to build data environments that encompass multiple information sources.

This approach allows organizations to develop their analytics solutions in a consistent and scalable way, regardless of whether the data resides in on-premises systems or cloud services.