🚀𝐃𝐢𝐫𝐞𝐜𝐭 𝐋𝐚𝐤𝐞 𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐌𝐚𝐝𝐞 𝐄𝐚𝐬𝐲 𝐰𝐢𝐭𝐡 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜-𝐥𝐢𝐧𝐤-𝐥𝐚𝐛𝐬 🚀

Ever wanted to create Direct Lake semantic models from Import semantic models and rebind existing Power BI reports to Direct Lake semantic models without recreating them from scratch?

I tested the 𝐃𝐢𝐫𝐞𝐜𝐭 𝐋𝐚𝐤𝐞 𝐦𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐧𝐨𝐭𝐞𝐛𝐨𝐨𝐤 using the "𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐋𝐢𝐧𝐤 𝐋𝐚𝐛𝐬" Python library released by Michael Kovalsky

👇👇 Check out my step-by-step video guide below, where I demonstrate the migration process 🎥

Here’s a quick overview of the steps I followed:

✅𝘿𝙤𝙬𝙣𝙡𝙤𝙖𝙙 𝙖𝙣𝙙 𝙄𝙢𝙥𝙤𝙧𝙩 𝙉𝙤𝙩𝙚𝙗𝙤𝙤𝙠
- I Downloaded the "Migration to Direct Lake" notebook from the Semantic_link_labs.
- I Imported it into my workspace.

✅𝐂𝐫𝐞𝐚𝐭𝐞 𝐚𝐧𝐝 𝐂𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐞 𝐚 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞
- I set up a lakehouse and added it to my Fabric notebook.

✅𝐒𝐞𝐭 𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬 𝐰𝐢𝐭𝐡𝐢𝐧 𝐭𝐡𝐞 𝐧𝐨𝐭𝐞𝐛𝐨𝐨𝐤
-I defined initial parameters for my existing import semantic model and the new Direct Lake semantic model to be created.

✅𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐏𝐨𝐰𝐞𝐫 𝐐𝐮𝐞𝐫𝐲 𝐓𝐞𝐦𝐩𝐥𝐚𝐭𝐞
- I run the code to create a Power Query Template file within the "Files" section of my lakehouse.

✅𝐂𝐫𝐞𝐚𝐭𝐞 𝐚𝐧𝐝 𝐏𝐮𝐛𝐥𝐢𝐬𝐡 𝐚 𝐃𝐚𝐭𝐚𝐟𝐥𝐨𝐰𝐬 𝐆𝐞𝐧𝟐
-I Created a new Dataflows Gen2 from my lakehouse, imported the Power Query Template from OneLake, and published it.

✅𝐄𝐱𝐞𝐜𝐮𝐭𝐞 𝐂𝐨𝐝𝐞 𝐟𝐨𝐫 𝐃𝐢𝐫𝐞𝐜𝐭 𝐋𝐚𝐤𝐞 𝐦𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧
-I ran the code to generate the new Direct lake semantic mode based on my Import semantic model.

✅𝐑𝐞𝐛𝐢𝐧𝐝 𝐭𝐡𝐞 𝐫𝐞𝐩𝐨𝐫𝐭 𝐭𝐨 𝐭𝐡𝐞 𝐃𝐢𝐫𝐞𝐜𝐭 𝐋𝐚𝐤𝐞 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐦𝐨𝐝𝐞𝐥
-I executed the code to rebind my existing report to the new Direct Lake semantic model.


This streamlines the Direct Lake migration process, making it possible to rebind existing Power BI Import mode models to Direct Lake models without recreating them 🎉




👉Documentation: https://lnkd.in/e7Nr-Ec3
👉Download the notebook: https://lnkd.in/eXkhzZY5
👉My LinkedIn post announcing Semantic Link labs: link