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Day: September 3, 2026

Compiling Power BI Calculation Groups

Published 2026-09-03 by Kevin Feasel

Phil Seamark looks at an optimization:

Power BI now avoids compiling calculation items that a query has already filtered out. The time saved is before the first storage engine event. It does not make the scans themselves faster.

Will I benefit? If your query filters a calculation group down to a subset of its items, it may compile faster. A single filtered group can benefit, although often only by a little. The largest gains are in models where calculation groups reference one another, because the engine used to expand combinations the query never needed. If your query has no calculation groups, or uses every item in them, there is nothing to prune.

Click through to see what the hubbub is all about and if it might affect you.

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From SQL to PySpark and Spark SQL

Published 2026-09-03 by Kevin Feasel

Andy Brownsword gives Spark a try:

I’ve spent years shaping data with SQL Server, however after pulling at the threads of Fabric I’m opening notebooks and finding PySpark.

At first glance the difference is stark, but it’s not quite the dramatic shift it appears. If you’re not familiar, let’s look at what’s very similar, and where the true differences are.

There’s plenty of nuance in the syntax differences and behavioral differences between the platforms, but Spark SQL is just as ANSI compliant at this state as pretty much any other platform, and PySpark feels a lot like a chained quasi-functional approach to SQL because of Spark’s Scala heritage.

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Full-Text Index Management Permissions

Published 2026-09-03 by Kevin Feasel

Emad Al-Mousa tests some permissions:

I was exploring SQL Server Text Indexes, and while exploring it I stumbled upon the function sys.dm_fts_index_keywords.

According to the “current” version of the documentation (up to 1 September 2026): https://learn.microsoft.com/en-us/sql/relational-databases/system-dynamic-management-objects/sys-dm-fts-index-keywords-transact-sql?view=sql-server-ver17

sysadmin role is required to run this function, I found out that this is not true !

Click through for the test. Granted, this does require CONTROL on the database, so not something that J. Rando db_datawriter can do.

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Atomicity and Isolation in Fabric Data Warehouse

Published 2026-09-03 by Kevin Feasel

Louis Davidson receives a surprise:

Ironically, today’s topic is kind of the opposite. I expected things to be far different in Fabric, but it isn’t really that different, at least not in behavior, except when it is. Since this system is Parquet file based, I didn’t think there would be locking, blocking, etc. I sort of expected it would be sort of locked down when writing data, maybe just single threaded per file, but highly concurrent when reading. Reads would most likely work like time travel and read previous data where it existed. And transactions? Would there be transactions? I guessed not before I got started with it.

Was I wrong?

Read on for the answer. The concurrency model isn’t exactly the same as SQL Server’s, but it’s not that far off.

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Spark SQL Temporary Views on Fabric Schema-Enabled Lakehouses

Published 2026-09-03 by Kevin Feasel

Gerhard Brueckl wants to create a temporary view:

Some time ago my friend Christian Henrik Reich blogged about how to handle schema-enabled lakehouses in Spark temporary views. We already found a good solution leveraging SQL USE keyword to set the schema once and reference tables by name only in our views. However, after some tests, in particular with notebookutils.notebook.runMultiple, I realized that there are some more things to consider as suddenly my SQL and notebooks stopped working when executed in parallel!

But let set the scope for this blogpost first. We recently built a data platform on Microsoft Fabric where we integrated data from different source systems which were combined into a single schema-enabled lakehouse where each source system had it own schema. Naturally, when querying those schemas, we used temporary views to express our business logic in SQL and then continue working with PySpark. We relied heavily on USE to work around the issue describe by Henrik until we realized that this approach does not work in combination with runMultiple. So we had to find another solution which I will describe here.

Read on for that solution.

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