Microsoft Fabric brings data integration, engineering, warehousing, real-time intelligence, data science and Power BI together in one data and analytics platform. We help you determine where Fabric fits, design the architecture and turn it into a platform that works in practice.
Microsoft Partner | Power BI | Microsoft Fabric | Azure
Microsoft Fabric is Microsoft’s end-to-end data and analytics platform. It brings together capabilities for collecting, transforming, storing, analyzing and visualizing data within one environment.
At the center is OneLake, the unified logical data lake for Fabric. Around it, Microsoft provides workloads for data integration, data engineering, data warehousing, data science, real-time intelligence and Power BI.
The idea is straightforward: instead of connecting separate tools for every part of the data journey, organizations can handle more of that work within one integrated Microsoft platform.
Microsoft Fabric becomes particularly interesting when your data challenges extend beyond reporting.
Perhaps data is spread across ERP, CRM, databases, Excel files and cloud applications. Transformation logic has gradually moved into Power BI. Different teams are rebuilding the same datasets or calculations. Your existing data warehouse has become difficult to maintain. Or you need a stronger data foundation for analytics and AI.
These are not problems you solve by adding another dashboard.
The starting point should be the problem you are trying to solve, not the technology you want to implement.
Microsoft Fabric covers much more than reporting. It brings together the different capabilities needed to move data from source systems to analytics, applications and AI within one platform.
Microsoft Fabric includes Data Factory capabilities for connecting to databases, files, cloud platforms, APIs and business applications.
You can use pipelines and dataflows to move data through your environment and bring information from different systems together in a more consistent way.
OneLake provides the shared storage foundation underneath Microsoft Fabric. Different Fabric workloads can work with data stored in the same logical data lake.
This helps reduce unnecessary duplication and makes it easier to reuse data across engineering, analytics, data science and Power BI.
Fabric supports both traditional data warehouse and lakehouse architectures. A warehouse provides a structured relational environment, while a lakehouse offers more flexibility for different types of data.
Which approach makes sense depends on the data, use cases and existing architecture. In practice, some environments will use a combination of both.
Raw data is rarely ready to use directly. It needs to be cleaned, combined, enriched and structured before people can rely on it.
Fabric provides data engineering capabilities for building these processes and moving important transformation logic out of individual reports and into a shared data layer.
Power BI is an integral part of Microsoft Fabric. Semantic models, reports and dashboards can sit directly on top of the data platform underneath them.
This creates a much closer connection between data preparation and analytics and can reduce the amount of transformation logic that ends up scattered across individual Power BI solutions.
Fabric also supports workloads beyond traditional reporting, including real-time analytics, data science and AI.
This makes it possible to work with streaming and event-driven data, build predictive models and create AI solutions on top of the same governed data foundation.
If you already use Power BI, Microsoft Fabric does not mean starting over.
Power BI is part of Fabric, and your existing reports, semantic models and analytics can continue to play an important role. Fabric mainly adds capabilities around Power BI, such as data integration, engineering, storage, warehousing, real-time analytics and data science.
That means you can expand the platform where it makes sense, without moving everything at once.
For example, you might keep your existing Power BI reports while gradually moving data pipelines, transformation logic or storage into Fabric. Or you may decide that your current data platform already works well and only use selected Fabric capabilities.
The important question is not how quickly you can move to Fabric, but which parts of your current architecture actually benefit from it.
Microsoft Fabric brings many data capabilities together in one platform, but it does not make the fundamentals of good data architecture disappear.
You still need to decide where data comes from, how it should be integrated and transformed, where business logic belongs, how history is handled and how trusted data reaches the people and applications that need it.
Fabric gives you technologies such as pipelines, lakehouses, warehouses, notebooks, semantic models and OneLake to solve those problems. The value does not come from using all of them. It comes from choosing the right components and giving each one a clear role in the architecture.
Your existing environment matters too. Azure services, databases, TimeXtender, Power BI and other platforms do not automatically have to disappear because Fabric is introduced. In many cases, the best architecture combines what already works with selected Fabric capabilities.
That is why we start with the data architecture and business requirements, not with a checklist of Fabric workloads.
Microsoft Fabric brings a lot of capabilities together, but that does not automatically make the architecture simpler. The challenge is deciding which parts of the platform you actually need, how they should work together and how Fabric fits with what you already have.
As a Microsoft partner, we help organizations design, implement and improve Fabric environments with a focus on the complete data architecture. That includes data integration, OneLake, lakehouses, warehouses, semantic models, Power BI, governance, security, performance and deployment.
Sometimes that means building a new Fabric environment from the ground up. Sometimes it means extending an existing Power BI, Azure or TimeXtender setup. And sometimes the best decision is to keep parts of your current architecture exactly where they are.
Our role is to help you make those choices deliberately, and then turn them into an environment your team can actually work with.
Microsoft Fabric can cover a large part of the data platform, but that does not mean every organization needs the same implementation. We help you determine where Fabric fits, design the architecture and turn that design into a platform your team can actually use and maintain.
Choose the right Fabric capacity and licensing setup
Microsoft Fabric is primarily licensed through capacity, while Power BI may also require user licenses depending on how the environment is configured. The right setup depends on your workloads, number of users, Power BI usage, expected consumption and wider architecture.
As a Microsoft partner, we can help you understand the available options, determine which capacity and licensing setup fits your environment and supply the required Microsoft Fabric and Power BI licenses directly.
Because licensing, capacity and architecture are closely connected in Fabric, we look at these choices together rather than treating licensing as a separate purchasing decision.
Design and improve your Fabric environment
Need help working out how Microsoft Fabric should fit into your data landscape? We help with architecture, workload choices, governance, performance and the practical design decisions that determine whether Fabric remains manageable as it grows.
That can mean designing a new environment, improving an existing implementation or helping your own team make better technical decisions.
Understand where Fabric actually adds value
You do not have to move everything to Fabric to benefit from it. We can assess your current data architecture, identify where Fabric capabilities make sense and determine what should stay where it is.
The result is a practical architecture based on your existing systems, requirements and priorities rather than a checklist of Fabric workloads.
Turn the architecture into a working platform
Once the direction is clear, we can help implement the parts of Fabric you actually need. That can include data ingestion, pipelines, OneLake, lakehouses, warehouses, semantic models, Power BI and the deployment and governance processes around them.
We focus on building an environment that your team can understand, operate and extend rather than creating unnecessary platform complexity.
Keep your Fabric environment reliable and manageable
Already running Microsoft Fabric? We can help with day-to-day issues, performance, capacity, pipelines, permissions, deployments and other technical challenges across the platform.
We can also stay involved as your environment develops, helping you improve it over time and make sensible decisions as new Fabric capabilities become available.
Not necessarily. If Power BI already meets your reporting needs and the data platform behind it works well, there may be no reason to introduce more Fabric capabilities.
Fabric becomes more relevant when the challenges extend beyond reporting, for example into data integration, engineering, storage, scalability, governance or advanced analytics. We can help assess where Fabric would actually add value and where your existing setup is already doing the job.
Read more about Microsoft Power BI and how it fits into the wider data platform.
It can be. Fabric brings a broad set of capabilities together in one platform, but that does not mean every organization needs all of them.
For a relatively straightforward reporting environment, Power BI and an existing data platform may be perfectly sufficient. Fabric makes more sense when you need to solve broader data challenges or want to consolidate capabilities that are currently spread across several technologies.
The starting point should be your requirements and architecture, not the size of the Fabric feature list.
Start with the problems you are trying to solve.
Fabric may be worth exploring if data is scattered across systems, transformations are duplicated, your Power BI environment is becoming difficult to manage, your current data warehouse is reaching its limits or you want a stronger foundation for analytics and AI.
We can review your current environment and help determine which Fabric capabilities are relevant, what can remain as it is and what a sensible next step would look like. Learn more about our Second Opinion for Data & Analytics.
Yes. You do not have to implement every Fabric workload at once.
You might start with Power BI and a warehouse, introduce Data Factory for integration, use OneLake as part of the storage architecture or add other workloads later as requirements develop.
In many cases, a selective approach is more sensible than trying to redesign the entire data platform around Fabric from day one.
Yes. A Fabric adoption does not have to be a big-bang migration.
Existing data warehouses, Azure services, Power BI models and other components can often continue to run while selected workloads move to Fabric. This allows you to validate the architecture, build experience and migrate where there is a clear benefit.
We generally prefer a phased approach that reduces risk and avoids replacing technology simply because a newer option exists.
No. Microsoft Fabric can become part of your existing architecture rather than replacing everything around it.
An existing SQL data warehouse, Azure environment or other data platform may still be perfectly suitable. In some cases Fabric can simplify or modernize parts of that architecture, while in others there is little benefit in moving something that already works well.
We look at the architecture as a whole and determine where Fabric fits rather than assuming it should replace every existing component.
Yes. Microsoft Fabric and TimeXtender can be used together.
TimeXtender can continue to provide data integration, transformation, orchestration and data management capabilities, while Fabric is used for areas such as storage, warehousing, Power BI or other analytics workloads.
The right division of responsibilities depends on your existing TimeXtender environment, the capabilities you already use and what you want Fabric to add. Read more about TimeXtender and how we use it as part of a modern data platform.
Not automatically. There is overlap between some Fabric and TimeXtender capabilities, particularly around data integration and data platform workloads, but that does not mean an existing TimeXtender environment should simply be replaced.
If TimeXtender already provides reliable integration, transformation and management processes, those remain valuable. Fabric may complement that environment, gradually take over selected workloads or become part of a longer-term migration strategy.
The right decision depends on architecture, skills, maintainability, migration effort and the practical benefits Fabric would provide. Our TimeXtender page explains more about the role TimeXtender can play in the wider data architecture.
Potentially both.
Fabric can simplify an architecture by bringing integration, storage, engineering, analytics and Power BI closer together. But introducing Fabric without clear architectural choices can also create another layer of technology alongside the systems you already have.
The goal should therefore not be to adopt as much Fabric as possible. It should be to reduce unnecessary complexity and give each part of the platform a clear role.
The right setup depends on the workloads you use, the number of users, how intensively the platform is used and how much compute capacity those workloads require.
Choosing a capacity is therefore not just a licensing exercise. Architecture, workload patterns and expected growth all influence what makes sense.
We can help determine an appropriate setup and can also supply Microsoft Fabric and Power BI licensing as part of the wider solution.
Fabric costs can be predictable, but they depend heavily on how the platform is designed and used.
Different workloads share capacity, and inefficient data processing, refresh patterns or architecture choices can increase resource consumption. Capacity can also be scaled, which gives flexibility but makes it important to understand actual workload requirements.
We therefore consider cost and capacity as part of the architecture rather than treating licensing as a separate decision at the end.
Yes. You do not need to start with a new implementation.
We can review an existing Microsoft Fabric, Power BI, Azure or broader data environment and look at architecture, workloads, data flows, semantic models, governance, performance, capacity and maintainability.
The result can be as simple as confirming that the current direction makes sense, or it may identify specific areas where the architecture can be simplified, improved or extended. This kind of review is also a natural starting point for a broader data & analytics consultancy engagement.
Yes. We can support Microsoft Fabric environments after implementation, whether we built the environment ourselves or your team or another partner did.
That can include troubleshooting, pipelines, refreshes, permissions, performance, capacity and ongoing improvements across Fabric and Power BI.
Read more about our Microsoft Fabric Support service.
Not sure where to start, what to migrate or which Fabric capabilities you actually need? We can help you make sense of the options and turn them into a practical data architecture. From licensing and architecture to implementation and support, we can help at every stage.