#SSAS #SqlServerAnalysisServices #TabularEditor question: did a recent patch break calculations for ya’ll? I’m suddenly getting “Value was either too large or too small for a Currency” on two different models, one of which I think doesn’t even have any currency-formatted measures or money data types.

Power Pivot Principles: The A to Z of DAX Functions – INFO.MEASURES https://zurl.co/Xvrc #SSAS #Azure #AnalysisServices #DAX

REALLY!? It was an issue of file path lengths? You know what, fine. FINE. I won’t use “long” filepaths for backup files, like anything close to 90 characters.

Really, #SqlServerAnalysisServices? You just couldn’t handle a file path that is barely longer than your default directory path for backups?

Sigh.

#SSAS

DuckDB. OLAP-куб в кармане

Привет, меня зовут Антон, я старший инженер в департаменте аналитических решений ЮMoney. В компании мы используем технологию MSSQL OLAP-кубов SSAS, которая хорошо себя зарекомендовала — она сравнительно легко развёртывается и достаточно производительная. Но есть ряд минусов: Microsoft прекратил развивать её примерно в прошлом десятилетии, технология требует производительных серверов, ну и, конечно, вопрос зависимости от иностранного вендора тоже стоит остро. Поэтому, посматривая по сторонам в поисках альтернативы, я решил попробовать недавно появившуюся технологию DuckDB. Особых надежд не было, но хотелось понять, на каком она уровне по сравнению с привычными для меня инструментами.

https://habr.com/ru/companies/yoomoney/articles/840624/

#duckdb #olapкубы #mssql #ssas #python

Every data witch needs a #DateDimension configured just the way she likes… or more likely, tailored to the idiosyncrasies of a bunch of end users. Here’s my basic version of that: https://github.com/Willow-DataWitch/DateDimension

#SSAS #TabularModel #TabularEditor #SqlServer

Preparing a presentation on VertiPaq and optimizing models for #PowerBI and #SSAS Tabular only served to convince me of one thing:

#DAX is the path to worse performance. Just don't write it if you can avoid it.

E.g., queries on a table of 8.5M rows are faster than the same on a table of 10K, when the larger needs no DAX and the latter does have some logic.

When it comes to the BI & data visualization change what I'm liking the most is the technical challenge to extract insights. Working with #SQL, #Python, #Pandas, #Excel, #SSMS, #SSIS, #SSAS, doing #ETL... It aint easy but it is rewarding. Also learning a lot using #Tableau, #Qlikview and #Talend.

Hardest and least appealing to work with so far has been #SSRS, but I like the results when I manage to get what I want :)