Explore data teams and Microsoft Fabric

Microsoft Fabric's unified data analytics platform makes it easier for data professionals to collaborate on projects. Fabric increases collaboration between data professionals by removing data silos and the need for multiple systems.

Traditional roles and challenges

In a traditional analytics development process, data teams often face several challenges due to the division of data tasks and workflows.

Data engineers process and curate data for analysts, who then use it to create business reports. This process requires extensive coordination, often leading to delays and misinterpretations.

Data analysts often need to perform downstream data transformations before creating Power BI reports. This process is time-consuming and can lack the necessary context, making it harder for analysts to connect directly with the data.

Data scientists face difficulties integrating native data science techniques with existing systems, which are often complex, and makes it challenging to efficiently provide data-driven insights.

Evolution of collaborative workflows

Microsoft Fabric simplifies the analytics development process by unifying tools into a SaaS platform. Fabric allows different roles to collaborate effectively without duplicating efforts.

Data engineers can ingest, transform, and load data directly into OneLake using Pipelines, which automate workflows and support scheduling. They can store data in lakehouses, using the Delta-Parquet format for efficient storage and versioning. Notebooks provide advanced scripting capabilities for complex transformations.

Data analysts can transform data upstream using dataflows and connect directly to OneLake with Direct Lake mode, reducing the need for downstream transformations. They can create interactive reports more efficiently using Power BI.

Data scientists can use integrated notebooks with support for Python and Spark to build and test machine learning models. They can store and access data in lakehouses and integrate with Azure Machine Learning to operationalize and deploy models.

Analytics engineers bridge the gap between data engineering and analysis by curating data assets in lakehouses, ensuring data quality, and enabling self-service analytics. They can create semantic models in Power BI to organize and present data effectively.

Low-to-no-code users and citizen developers can discover curated datasets through the OneLake Hub and use Power BI templates to quickly create reports and dashboards. They can also use dataflows to perform simple ETL tasks without relying on data engineers.