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What is Microsoft Fabric? Part 1: From the past to the present

 A man sitting at a desk, different reports on two screens

Microsoft released its new data platform on 23 May 2023. I was in Keilaniemi in May when Fabric was unveiled, which was said to be the most significant thing in our industry since SQL Server. What should one think about this? 

Microsoft’s most important BI solutions before Fabric

Do you remember the days when Cognos and Business Objects were the first things that came to mind when you thought about data warehouses and advanced reporting? And the fact that they cost a fortune. The BI landscape changed radically in 2005 when Microsoft offered a reasonably priced alternative, and introduced SQL Server 2005 and its revamped integration, analysis, and reporting services. 

In fact, several people jumped aboard Microsoft’s Power BI platform at the time, even though it wasn’t the best one to begin with. In addition to the price, the attractiveness was also enhanced by confidence in Microsoft. Pretty quickly, the choice proved to be a winning one. 

But Microsoft’s data journey has not been without its complications. New innovative players emerged in the field of data analytics for all. Competitors such as Qlik, with its in-memory data models and impressive reports, took interactive reporting to a new level, and was a pretty tough competitor well into the 2010s. While the Microsoft data platform was functional, the implementation and presentation layer of reports and analysis was fragmented in Sharepoint, PerformancePoint Service, Excel, and others. That all changed when Microsoft put Power into its offering.

Microsoft Power BI, released in 2015, combined the then scattered data functionalities into a single product that evolved on a monthly basis. The product made preparing reports pleasant for coders as well as for business people. Power BI also put Microsoft in the lead in reporting.

Data platforms evolve to meet expanded needs 

New business requirements and cloud computing have changed the world over the years, also data platforms. Scalability, near real-time machine learning models, diversity of source data and of course cost are all factors to consider when choosing data platforms. Before Fabric, Microsoft’s offering required many services depending on the situation, too, such as Azure SQL, Azure Data Factory, Azure Data Lake, Synapse, Stream Analytics, Azure Databricks, etc. Moreover, in this time frame, new innovative competitors such as Snowflake have also emerged in some areas to change the battlefield of data platforms.
  
Microsoft Fabric combines modern data warehousing and reporting into a single product with its new cloud services product. Fabric is still in the preview phase, but Microsoft’s goal is to turn a fairly confusing puzzle into one clear product. And its success would be far from the first time in Microsoft’s history. 

This article is the first part of a blog post series on Microsoft fabric. In future parts, I will go into more detail about the technology of the solution, and also test its functionalities. Microsoft Fabric meets the platform needs of data storage and reporting in a single solution, but what is the technical innovation behind it, or is there any? I will tell you more about this in the second part of this blog post series.  

To find out more about Fabric’s possibilities, book an appointment for a  discussion

Read more: 

What is Microsoft Fabric? Part 2: Technology
What is Microsoft Fabric? Part 3: Performance
What is Microsoft Fabric? Part 4: Licenses
What is Microsoft Fabric? Part 5: Fabric is Microsoft’s favorite child and it is developed rapidly
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Microsoft Fabric and AI make it easier for educational institutions to manage data and self-service reporting
What is Microsoft Fabric?  
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Jani Laitala

Jani Laitala

I work at Pinja as a data architect, implementing both data warehouse solutions and reporting. I am interested in the information in the data and I want to communicate it with visuals. In our free time, our family is busy with both culture and sports.

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