Hi Guys, I have noticed a problem with a solution I have deployed using Fabric. I am using a trial capacity (FT1). I am using a medallion architecture with a data pipeline to orchestrate this process. On the bronze layer, I have a dataflow that pulls the latest data from a web source as a raw table and store this data in a lakehouse (this seems to always work). However there is another dataflow that pulls the data from the bronze lakehouse and then performs cleansing on this source. This is where the problems start as some of the time this works just fine and the latest raw data from the bronze lakehouse is cleansed into my silver lakehouse. However there are times where it doesn't bring in the latest data pulled from the bronze lakehouse. This is the M-code to access the bronze lakehouse data: let Source = Lakehouse.Contents([]), #"Navigation 1" = Source{[workspaceId = "c79766a3-4f30-43d3-942c-d1fa4e84b64d"]}[Data], #"Navigation 2" = #"Navigation 1"{[lakehouseId = "72ab087e-c6f4-40dd-8149-95bd9c8e5bb5"]}[Data], #"Navigation 3" = #"Navigation 2"{[Id = "Files", ItemKind = "Folder"]}[Data], It is very frustrating as it makes no sense.

Microsoft has released the official self-paced course for DP-700. Check it out here👉🏽 Course DP-700T00-A: Microsoft Fabric Data Engineer This course will eventually become the recommended self-paced material on the Fabric Data Engineer Associate certification page. So, if you plan on taking the exam once it goes GA, you might as well take advantage of this course and start preparing now. Also, find some helpful DP-700 discussions below: - DP-700 beta exam experiences and tips (by Ali Stoops) - DP 700 - Study Guide & Learn Collection When are you planning to take the DP-700 exam? Let us know how you’re preparing below👇🏽

Hi Guys, Just wanted to ask about Partitioning with Direct Lake. I already have a very large delta table, roughly 60 million rows. Every hour I am appending data to this table using a notebook. I have partitioned this table using year and month (so roughly 84 partitions). I assume the benefit of partition is that the append is easier and the optimize function doesn't have to join up the 60 million rows but rather the append files inside of the latest year+month combination. However when I go to the Microsoft guide it tells me that I should avoid using partitions if my goal is to use a delta table for a semantic model (which it is): Microsoft Reference: https://learn.microsoft.com/en-us/fabric/get-started/direct-lake-understand-storage#table-partitioning Important If the main purpose of a Delta table is to serve as a data source for semantic models (and secondarily, other query workloads), it's usually better to avoid partitioning in preference for optimizing the load of columns into memory. Questions: 1. Should I avoid using the partition? 2. What examples are there of why we need to partition? Any help will be much appreciated. Thanks

Just wondering how long you guys spent studying for the DP-600? What resources did you use?

Hey everyone, I am currently using a Fabric Trial License (FT1) and I was wondering what is the best License to get given my current consumption. I have attached a screenshot of my Fabric Capacity Metrics and I can see the highest total usage occurred on 1st October @ 10:31. I used 91.27 CU: (Interactive CU: 9.97, Background CU: 81.3) in a 30 second period. This seems to indicate I need a F4 SKU? As 91.27/30 = 3.04.... However, I notice that my background consumption was highest a few minutes later at 83.87 CU in a 30 second period. Whereas my interactive CU was highest on 10th October at 78.48 CU in a 30 second period. The sum of these two highs returns a 162.35 CU, which would indicate I need a F8 SKU? As 162.35/30 = 5.41.. Which SKU do you think I need? Furthermore if I want to reduce my consumption, how would I go about doing this? For background operations when I drill-through at the highest consumption point I see multiple runs of my notebook for different periods. Why? For interactive operations I see a Query which ran 5 minutes before the drill-through time. Why? Any help would be much appreciated.