Pros and cons of moving from SQL to Fabric OneLake discussed. Focus on Power BI dev's ability to push to SQL. Different perspectives on using Fabric OneLake for data engineering tasks explored. Importance of Excel in the financial industry highlighted. Limitations of CSV files in data analysis discussed. Lake house concept and Microsoft's One Lake examined.
Fabric enables business users to perform data engineering tasks, bridging the gap between business users and data engineers.
Fabric combines the benefits of data lakes and data warehouses, providing a unified platform for data storage, analytics, and reporting.
Fabric empowers business users to refine and enhance data, collaborate with data engineers, and take ownership of the data engineering process.
Deep dives
Fabric and the Blurring Line Between Business Users and Data Engineers
The podcast episode explores the capabilities and implications of using fabric, a new tool that enables business users to perform data engineering tasks. The hosts discuss the role of fabric in bridging the gap between business users and data engineers, allowing business users to manipulate and refine data in a way that was traditionally done by data engineers. They highlight the ease of integration and the ability to push data back into systems, enabling business users to have more control over their data and contribute to the data engineering process.
The Evolution of Data Storage and Analytics
The hosts delve into the concept of lake houses, which combine the benefits of data lakes and data warehouses. They discuss the potential of fabric in providing a unified platform for data storage, analytics, and reporting. They highlight the power of fabric in enabling business users to connect directly to data sources, manipulate and transform data, and generate insights without relying heavily on data engineers. They emphasize the shift towards the use of fabric for reporting purposes and its ability to enhance data governance and data quality.
The Role of SQL in the Fabric Framework
The hosts explore the role of SQL in fabric and its significance in the data engineering process. They discuss the potential of fabric in empowering business users to perform SQL transformations and apply data quality rules directly in the fabric environment. They also highlight the capabilities of fabric in allowing business users to integrate with existing systems, push data back into source systems, and enhance data within the fabric framework. They emphasize the evolving nature of fabric and its impact on data storage, analytics, and reporting.
Enabling Business Users with Fabric
The hosts discuss how fabric empowers business users to perform data engineering tasks and contribute to the data pipeline process. They emphasize the ease of integration and the user-friendly interface of fabric, which allows business users to create notebooks, build pipelines, and manipulate data in a seamless manner. They highlight the potential of fabric in enabling business users to refine and enhance data, and the opportunities it presents for collaboration between business users and data engineers. They stress the importance of data governance and data quality in the fabric environment.
The Future of Data Engineering with Fabric
The hosts envision a future where fabric becomes a central tool for data storage, analytics, and reporting. They discuss the potential of fabric in enabling business users to take ownership of their data and contribute to the data engineering process. They highlight the possibilities of fabric in pushing data back into source systems, leveraging SQL, and enhancing data governance. They emphasize that fabric is a powerful tool that bridges the gap between business users and data engineers, providing a unified platform for data manipulation, transformation, and analysis.
Mike, Seth, & Tommy weigh the pros and cons of transitioning from SQL databases to Fabric Lake Houses. They dive into the differences between the two data platforms, and explore the factors that organizations should consider before making the switch. Major focus on the ability for the Power BI dev to push to out to SQL - what does that mean?
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