The podcast discusses the integration of data into a fabric lakehouse and the benefits it brings. They explore the concept of a lake house, the layers of data within it, and the challenges of managing permissions and governance. They also touch on the integration of Synapse and Data Factory into Power BI, and the new roles added for real-time model training. Overall, they provide insights into how much data should be integrated into a lakehouse and its impact on businesses.
Having a clear plan for data governance and management is crucial when implementing a fabric lakehouse.
Enhancements to the data set refresh history provide users with more insights and troubleshooting capabilities.
A single lakehouse can serve as the central storage and compute layer for different business units, simplifying access to data and allowing for efficient data engineering processes.
Deep dives
The purpose of the fabric readiness repo
Microsoft has released the Microsoft fabric readiness get repo, which provides presentation decks for user groups, online presentations, in-person conferences, and customer conversations. The repo covers topics such as data science and fabric, data engineering and fabric, real-time analytics with fabric, and data integration with data factory inside fabric.
Improvements in data set refresh history tracking
Microsoft has made enhancements to the data set refresh history, providing users with more details and information. Users can now see when a refresh started and stopped, the type of refresh (data or query cache), and the duration of each refresh. This new feature helps with troubleshooting and provides more insights into the data refresh process.
The importance of data governance and planning in fabric implementation
When implementing fabric, it is crucial to have a clear plan for data governance and management. This includes determining how much data should be loaded into the lake house, defining ownership and stewardship of data, and establishing proper permissions and access control. A well-thought-out strategy ensures the success and effectiveness of fabric implementation.
Building a single lake house for business unit data needs
To support different business units' data needs, it may be beneficial to have a single lake house that serves as the central storage and compute layer. This allows each business unit to build their own tables and artifacts within the lake house while maintaining data governance and ownership. By doing so, it simplifies access to data and allows for efficient data engineering processes.
Exploring the potential of fabric as a tool for data discovery and reporting
Fabric provides a simplified and user-friendly experience for data discovery and reporting. With its ability to connect to various data sources, including APIs and Excel files, fabric allows users to easily pull data together, build models, and create queries. It empowers subject matter experts within the organization to explore and analyze data, making reporting and insights more accessible to a wider audience.
Now that we are all familiar with a Fabric Lakehouse.... How much data should be integrated into it? Enough for 1 Power BI Dataset? Enough for 20?
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