Data teams and Microsoft Fabric
Microsoft Fabric's unified data analytics platform makes it easier for data professionals to work together on data projects. Fabric removes data silos and the need for access to multiple systems, enhancing collaboration between data professionals.
Traditional roles and challenges
In a traditional analytics development process, data engineers and data analysts face several challenges. Data engineers perform complex data processing and then curate and serve data sources so data analysts can display data effectively for the business. This process requires extensive communication and coordination between the two roles, often leading to potential delays and misinterpretations.
Data analysts need to perform extensive downstream data transformations before creating Power BI reports. This time-consuming process often lacks context, making it difficult for analysts to connect with data directly.
Data scientists also struggle to integrate native data science techniques with existing data systems, which are often complex and cumbersome. As a result, data scientists find it challenging to provide data-informed insights efficiently.
Evolution of collaborative workflows
Microsoft Fabric transforms the analytics development process by unifying tools into a SaaS platform, allowing flexibility for different roles to perform necessary skills without duplicating efforts.
Data engineers can now ingest, transform, and load large amounts of data into OneLake and present it in whichever data store makes most sense. Data loading patterns are simplified using pipelines and architectures, such as medallion, can be easily configured using workspaces.
Data analysts gain greater context and streamline processes, transforming data upstream with Data Factory and connecting with data more directly using DirectLake mode.
Data scientists Integrate native data science techniques more easily and use Power BI's interactive reporting to provide data-informed insights.
Analytics engineers bridge the gap between data engineering and data analysis by curating data store assets, ensuring data quality, and enabling self-service analytics.
Low-to-no-code users and citizen developers can now discover curated data through the OneLake hub, and further process and analyze it to suit their needs without being dependent on data engineers or duplicating data.