Hi falks, I am planing to create a capacity for dev/qa environemnts and a second one for production where we plan to locate the gold zone for semantic model and powerbi consumption. The question is, should be booth capacities of the same size? Should be the reporting capacity bigger than the capacity for data engineers?

I'm taking the 30-day Spark course, but I still have a few doubts, which are as follows: DataFrame filtering To read the contents of a table into a dataframe we can write: df = spark.sql( “SELECT * FROM SparkSetember.propertysales LIMIT 1000” Subsequently, we can, for example, Filter the table with the function: df.filter(df.City.startswith(“L”)).show() My question is this: Why don't we do it straight away: df = spark.sql( “SELECT * FROM SparkSetember.propertysales WHERE Citi Like ‘L%’ ”

Thank you will for putting together this awesome content together. I passed my DP600 certification today. Couldn't have passed it without

Microsoft just released a new tutorial on implementing Type 2 SCD with a Dataflow: 👉🔗 https://learn.microsoft.com/en-us/fabric/data-factory/slowly-changing-dimension-type-two

Hey everyone, this week, Microsoft released the August update list, you can find them all here: https://blog.fabric.microsoft.com/en-gb/blog/microsoft-fabric-august-2024-update?ft=All What are your thoughts? Which feature stands out for you? Let us know below! 👇