Since Microsoft Fabric entered our lives, the rules of the data game have changed. Your data now lives as a Single Copy in OneLake, stored in open Delta Parquet format. But here’s the truth: 👉 The storage layer is unified. 👉 The compute engine is the real strategic choice. As a Data Engineer, how do you choose the right architecture? Let’s break it down. 🏛️ 𝟭. 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲: 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗧-𝗦𝗤𝗟 𝗮𝗻𝗱 𝗦𝘁𝗿𝗶𝗰𝘁 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 If your project demands high discipline, transactional integrity, and a fully structured environment — this is your domain. 𝙒𝙝𝙮 𝘾𝙝𝙤𝙤𝙨𝙚 𝙄𝙩? Full DML support directly via SQL: 𝗜𝗡𝗦𝗘𝗥𝗧, 𝗨𝗣𝗗𝗔𝗧𝗘, 𝗗𝗘𝗟𝗘𝗧𝗘, 𝗠𝗘𝗥𝗚𝗘. You can build controlled, deterministic data pipelines entirely in T-SQL. 🔐 𝙏𝙝𝙚 𝙎𝙚𝙘𝙧𝙚𝙩 𝙒𝙚𝙖𝙥𝙤𝙣: 𝙈𝙪𝙡𝙩𝙞-𝙩𝙖𝙗𝙡𝙚 𝙏𝙧𝙖𝙣𝙨𝙖𝙘𝙩𝙞𝙤𝙣𝙨 Execute complex business logic via: Stored Procedures Explicit Transactions (BEGIN TRAN, COMMIT) Enterprise-grade schema enforcement Perfect for finance, ERP, and systems that demand strict consistency. 🌊 𝟮. 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲: 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 If you’re dealing with massive datasets, semi-structured data (JSON, Logs), or ML-heavy workloads — the Lakehouse shines. 𝙒𝙝𝙮 𝘾𝙝𝙤𝙤𝙨𝙚 𝙄𝙩? Process unstructured/semi-structured data easily. Use Spark + Python for scalable engineering. Leverage distributed compute for heavy transformations. ⚠️ 𝗧𝗵𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗗𝗶𝘀𝘁𝗶𝗻𝗰𝘁𝗶𝗼𝗻 You can query Lakehouse tables using the SQL Analytics Endpoint, but it is Read-Only. Writes and transformations happen through: Spark Notebooks Spark Job Definitions Dataflows Gen2 SQL here is strictly for analytics and verification, not for data manipulation pipelines. ⚡ 𝗧𝗵𝗲 𝗦𝗵𝗮𝗿𝗲𝗱 𝗣𝗼𝘄𝗲𝗿: Direct Lake Mode Both Warehouse and Lakehouse support Direct Lake. Power BI reads directly from OneLake Delta files—no import, no refresh cycles, near real-time performance. 🚀 𝗧𝗵𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗮𝘁𝗿𝗶𝘅 Make your decision based on three pillars: 1️⃣ Team Skillset T-SQL heavy team ➔ #Warehouse

Hi! Have a client that is implementing Fabric in the lowest capacity possible due to budget constraints. To try to maintain costs under control we are trying to find the best practices or best way to bring data from Lakehouse into the Warehouse that will minimize CU usage, can be scheduled, needs to have incremental refresh and uses SQL to get data.We've tried a bit with a Copy Activity and SQL queries inside this activity, which achieves most of the requirements but it seems that CU usage is still high. Any recommendations? We are thinking about trying stored procedures and schedule it through a pipeline...any other ideas?We have not considered mirroring because most of the tables have no column fro date / time from source systems.

Hello i am new Power BI and have to migrate Power BI to MS Fabric. What user permissions needed for the migration is workspace admin enough or we need even tenant admin as well. once the tenant of the MS fabric created just reassigning the workspace from Power BI MS fabric will do ? or do we have any step by step documents which can help me to achieve this. Also i do have some of the workspace where i am the ownder and for am not the owner. How do i identify who is owner when i do see the contact details for that workspace. what all are the pre checks i need to take care

Hi, I have a model that should be direct lake but when looking at the Gateway and Cloud connections in settings is it using a SQL endpoint. Normally this would show a string containing AzureDataLakeStorage as my other direct lake models do. I'm using git integration so I could look at changing .tmdl configurations. Any suggestions appreciated.

Is there any way to encrypt the warehouse table column in Fabric?