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What is Microsoft Fabric? Microsoft Fabric is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. It offers a comprehensive suite of services including data engineering, data factory, data science, real-time Analytics, data warehouse, and databases. It sets a new standard for data platforms.

Microsoft Fabric was released at the end of last year and has since generated significant interest. There are good reasons for this. At first glance, it may seem like just a rebranding of existing products, but the more you delve into the platform, the more you realize the change is far more substantial.

It’s fair to say it sets a new standard for data platforms, making life easier and better for everyone in the data chain. This applies whether you are a data engineer, report developer, AI developer, data consumer, or someone with overall responsibility, needing to balance costs and investments.

Drivers for moving towards Fabric

In a data road map analysis, there can be multiple factors suggesting moving towards Microsoft Fabric, but individually they might not be clear enough to make the leap. In this blog, we will provide some different drivers to help guide your decision. Depending on your current environment, recent investments, and what is most crucial for your business, you may evaluate these factors differently. By looking at some major drivers both collectively and individually, you may find the right path for your business on how to approach this new concept. Here they are:

Simplification and performance as driver

Inside Fabric, OneLake is a central concept. As the name suggests, it’s a place for all your data. Microsoft often uses the analogy of OneDrive to describe what OneLake does. Just as you can manage all your files in OneDrive, you can manage all your data in OneLake. You can even use File Explorer in Windows to get an overview of your data. This is a good example of a recurring theme in Fabric – simplifying administration and user experience, making previously technical tasks accessible to non-technical users or citizen developers. Ultimately, this means democratization – making it easier for more consumers to access data.

Another key feature is that Fabric achieves something entirely new by using the VertiParq engine together with the Delta Parquet format in OneLake. This combination makes it possible to realize something that has always been a difficult trade-off – efficient use of storage space along with strong query performance. Queries can now be run directly on compressed data in OneLake with good performance. For users, this opens the possibility of reducing or eliminating the need for data copying and duplication that may have been required for performance reasons in legacy systems. The Direct Lake concept for making data available to Power BI is an example of how this opportunity can be used.

It’s fair to say it sets a new standard for data platforms

A third new component is the simplified process of loading data from external systems. With the concept of shortcuts to external systems, you might completely avoid or at least simplify ETL processes (Extract, Transform & Load) to collect external data. With the concept of mirroring, you can access an updated and mirrored copy of an external database without writing any special code for this.

Updates are managed automatically using change data capture technology. In these ways, the amount of need for manually constructed transformations decreases, thereby reducing technical debt while improving data quality.

Read more on: How you can achieve results with Columbus pre-built data models, a link between Dynamics and Microsoft Fabric. 

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Better AI with proven data quality

We have seen many AI pilots over the past year, often with limited and quite general use cases. The natural next step is to focus on use cases that create real competitive advantages, which involves leveraging data from our own unique business operations. This is where we see Fabric playing a key role. One might say that if AI is our goal and our data is our fuel, then we need a vehicle to make the journey that goal. We believe that vehicle is Fabric, making it a natural part of our AI efforts.

To succeed with AI, we need to simplify the heavy but essential task of acquiring, organizing, and refining the data that underpins our AI applications. Fabric's components help us achieve controlled and proven data quality, ensuring that AI processes the right data in the right way – in other words to create responsible AI. By simplifying this critical task, we can spend more time building unique competitive advantages. For instance, when using Azure AI Studio, it is fully integrated with Fabric.

Ask open questions with Copilot

Within Fabric, Copilot support for data engineering lowers the barrier to building better and more efficient data pipelines, with less overhead from code reviews and testing iterations. There is also Copilot support for data science, aiding for instance in the development and execution of predictive models.

Perhaps the most exciting Copilot feature is the ability to support a conversation with data as part of Power BI. This feature is currently available only in preview and requires a higher license. The use cases that this feature opens are very appealing to people working as analysts, or working in support roles to decision making, or people being decision-makers.

Typically, the starting point might be that a problem owner only has access to prefabricated or static reports that can only partially provide the answers needed for a given problem statement. When the fixed format of these reports is too restrictive for idea generation or more specific exploration, the consumer is left without a simple solution.

While this limitation may sometimes be intentional, the ability to ask questions in natural language and to be able to track down more complex root cause analysis significantly enhances the analysis process, leading to faster and better insights. The Copilot capability in Power BI creates freedom for spontaneous questions to be answered instantly, for example during ongoing meetings or discussions. What question would you ask? Why has x or y performed better or worse this year?

Easier to control costs

A trend among consumers of data platforms has for some time been a reduced willingness to pay for storage but a continued acceptance of paying for computing. This is a logical consequence of scenarios like data collection from a company's products when used by customers, creating vast amounts of data. This data holds no value at rest but becomes valuable only once activated. Fabric enables working in this manner and obtaining a cost structure that matches the value structure.

Historically, data activation has involved various types of server allocation and data duplication, often accompanied by complex cost calculations and even guesswork about what the costs will be. The licensing model of Fabric is significantly simplified, especially when used with Power BI. Depending on your current data platform situation, this alone can be reason enough to consolidate all data processing under Fabric.

A simpler cloud journey

If you are currently using on-premises solutions for your data platform but plan to move to the cloud, you’re in luck with the timing. Transitioning from on-premises to a modern cloud-based data platform has long been a strong business case for reasons such as simpler administration, better scalability, security, and cost.

However, with Fabric, you can now achieve a whole new level of simplicity and cost optimization. This means you can more quickly reach goals like modernized decision support or entirely new AI applications. A more homogeneous and straightforward tech stack reduces the technical debt in the upcoming data platform. This will free up time and resources for the data team to invest more time in building competitive advantages.

Building a solid foundation moving forward

The company's ongoing and future innovations in data must be built on a solid foundation. With this in mind, we suggest looking into Fabric, regardless of if you are about to evaluate and choose a new platform or have recently invested in one to which you are currently committed.

The concepts that come with Fabric, and the suite of AI tools, are very compelling and will undoubtedly inspire and drive development across the entire industry. You might even consider using parts of Fabric right now, even if you cannot change everything now due to the schedule of a larger investment cycle.

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