Microsoft Fabric: the future of company data and business intelligence

Uses of Microsoft Fabric 

Data Engineering

A key component of Microsoft Fabric is the data engineering part of the platform. This area enables companies to create data pipelines in order to extract, transform and load data from different sources (ETL process). Here you can bring the data into the desired format so that it can be analyzed effectively and used in modern reporting tools such as Microsoft Power BI.

Different workloads are covered in the data engineering part of Microsoft Fabric:

• Data lakehouse: Storing and managing structured and unstructured data in one single place. 
• Data pipelines: Creation of data pipelines that carry out different actions consecutively, including data copying, transformations and integrations with other tools. 
• Apache Spark: Batch and streaming jobs can be carried out via Apache Spark. 
• Data preparation with notebooks: Microsoft Fabric makes it possible to use notebooks to carry out data extraction and transformation using different programming languages such as Python, Scala or R. This facilitates the development of models and data analysis. 
• Data warehousing: Data engineering enables the creation of data warehouses, in which data from heterogeneous sources can be centrally stored and easily retrieved.

Data Analysis

Once the data has been successfully prepared, it is time to create an efficient data model. This model comprises fact tables, dimension tables and the relationship to each other, and follows proven methods for data modeling. In Microsoft Fabric, users can now access this data and implement queries using visual query without needing extensive SQL knowledge. The best thing about this is that the data modeled and processed in the data engineering part can be seamlessly integrated into Power BI. This opens up the possibility of creating impressive visualizations and informative reports that form the basis for data-driven decision-making.

• Git integration: The new git integration enables developers to integrate their development processes, tools and best practices directly into the Microsoft Fabric workspace.
• Data Activator: Data Activator makes it possible to trigger actions based on data events. This may include notifications if certain conditions are met, such as customers in arrears or low stock levels.
• Copilot: An interesting feature is Copilot, which can be integrated in Power BI. Copilot uses natural language processing to answer questions in natural language, which enables interactive communication with the data.
• Direct Lake mode: Direct Lake mode combines import mode and DirectQuery mode and offers the best of both worlds to retrieve data directly from the data lake and process it.

Data Science

In the data science module of Microsoft Fabric, we concentrate on three key functions in particular:

Data preparation

An outstanding feature is Data Wrangler, which supports data scientists in data preparation. This tool offers functions for data preparation and automatic code generation, which makes data manipulation and transformation considerably easier. It offers a grid-like data view, dynamic summary statistics, integrated visualizations and a library of common data cleansing actions. This enables transformations to be carried out quickly and the data view to be updated in real time.

It is particularly helpful that Data Wrangler is geared toward machine learning, thereby simplifying complex data processing tasks for data scientists. It also offers a preview function for visualizing the effects of transformation steps, and automatically generates code snippets for reusable functions.

Experimenting and modeling

For efficient experimentation and tracking, Data Wrangler offers the option of organizing multiple versions in one experiment. This makes it easier for data scientists to assess performance by comparing versions and models within an experiment. Experiments are tracked via MLflow API, which facilitates integration in the data science process. Microsoft Fabric also offers a user-friendly graphical interface for filtering and managing experiments.

Data enrichment

Once a model has been developed, it can be used to enrich data in the data lakehouse. Using models is simple and can be done either by copying the generated code or with the help of the model assistant. These models can be used directly to enrich the data lakehouse and can be distributed immediately, which speeds up data processing and enables data-driven decision-making.

How companies can benefit from Microsoft Fabric

Microsoft Fabric offers many advantages, ranging from increased efficiency to the creation of a competitive advantage. Companies that deploy this technology wisely can revolutionize the way they use data and thus strengthen their competitiveness.

• Ready-made infrastructure: Opting for Microsoft Fabric means that you don’t need to provide the infrastructure yourself. Instead, the complete infrastructure is provided within Microsoft Fabric.
• Licensing models: Microsoft Fabric offers various licensing models, including individual licenses (free and Pro licenses) and organizational licenses (PPU and capacity licenses) with advanced functions for collaborative working. This enables flexible selection to suit the requirements of your organization.
• Power BI report: A Power BI report, which is integrated in Microsoft Fabric, enables insights into the use of resources to enable the best possible selection of licenses and resources.

We will support you as you prepare to start using Microsoft Fabric. Take advantage of our data value assessment to recognize the added value of your data. As part of the Telefónica Tech group, we also offer comprehensive support with your digital transformation, whether via automation, data analysis or cybersecurity. Get in touch with us today to find out more and start your journey to maximizing your business intelligence.