Hi all, I have a client that is currently using databricks and power bi. They have multiple clusters of databricks running and want to consolidate into one analytics platform. I've suggested Fabric, what would be the reasons for migrating over to fabric instead of using databricks? or would a hybrid approach be better, by using fabric and databricks? Will Fabric be cheaper to run as one platform? Please let me now your thoughts.

Hey everyone, a friend of mine Malvik Vaghadia has just released a brand new Udemy course on Microsoft Fabric. I've reviewed the course, and it's really comprehensive, and Malvik is a great instructor with lots of experience teaching on Udemy and on his YouTube channel. Try this link for FREE access (first 100 only, first come first served): 👉🔗 EDIT: *Link Removed Because All Coupons Have Been Redeemed* You can then use this link for a heavy discount (68% discount, I believe): 👉🔗 https://www.udemy.com/course/microsoft-fabric-the-ultimate-guide/?couponCode=LEARNFABRIC 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂'𝗹𝗹 𝗟𝗲𝗮𝗿𝗻: ✅ Data Warehousing: Understand the principles of data warehousing, master SQL for querying and managing data, and learn how to design and implement data warehouses within Microsoft Fabric. ✅ Power BI: Create powerful, interactive reports and dashboards. ✅ Data Engineering: Develop robust data engineering pipelines using Spark (PySpark), manage large-scale data transformations, and automate workflows efficiently. ✅ Data Factory: Master the use of Data Factory for orchestrating and automating data movement and transformation. ✅ Real-Time Intelligence: Leverage real-time data processing capabilities to gain instant insights and act swiftly in dynamic business environments.

We have DEV, QA and PROD workspaces. We have a power bi report connecting to a lakehouse in the DEV workspace. When we deploy to the QA workspace, the power bi report points to the lakehouse in dev. How do we change the report to point to the lakehouse in the QA workspace?

Happy Monday everyone! For those of you exploring metadata-driven architectures (which I think is quite a lot of you!)... here are some ideas for you: As a quick recap: the metadata-driven data pipeline is a technique commonly used in data engineering. Rather than explicitly declaring the source and the destination for a Copy Data Activity (for example), we instead design our pipelines so that the Source and Destination can be passed in dynamically. This means we can store details of the Source/Destination connections in another location, which is read at Execution time. This adds a lot of benefits: scalability, maintainability, and many more. However, the point of this post was to start a discussion about how/ and where you can store such metadata. The two most common ways you see metadata stored (in a Microsoft environment) are 1. In structured tables (like the Data Warehouse) 2. In a JSON File (perhaps in your Lakehouse Files area). However, I'd like to throw in a third option for discussion: storing your metadata in a Notebook (and passing it into your pipeline using mspsarkutils.notebook.exit(). Pros of this appoach: - make your configuration trackable by version control (which is not possible with the previous two methods) Cons: - maybe more difficult to read, if you have quite a few Key/Value pairs Thoughts? Where are you storing your metadata at the moment?

Is there a native way in Fabric to do incremental loads from SQL to a Lakehouse? I see there are native settings in azure ADF, but not in Fabric pipelines? Do you manually have to enable CDC on sql server and then add the sql code in the source tab?