Press "Enter" to skip to content

Category: Analysis Services

Differences in Logging between Azure Analysis Services and Power BI PPU

Gilbert Quevauvilliers continues a series on migrating from Azure Analysis Services to Power BI Premium Per User:

Another important aspect when having datasets is being able to log and monitor performance. In this blog post I am going to compare the logging between Azure Analysis Services (AAS) and Power BI Premium Per User (PPU).

With the recent release of PPU having integration with Log Analytics it makes it a lot easier to compare the logging options between AAS and PPU.

This is an area where there’s still a bit of a gap. Click through to see what the differences look like today.

Leave a Comment

Migrating Historical Data from Azure Analysis Services to Power BI Premium Per User

Gilbert Quevauvilliers continues a series on moving to Power BI Premium Per User:

In this blog post I am looking at how to load or reload historical data in AAS and PPU and compare the differences.

It should already be noted that I am only going to compare tables where I have partitions created and enabled. The reason being for dimension tables it is typically quick and easy to reload the data by re-processing the data for the table.

Read on for the details.

Leave a Comment

The Power BI Premium vs Azure Analysis Services Gap is Closing

Marco Russo has an update:

Almost 18 months ago I compared Azure Analysis Services and Power BI Premium for large datasets. At that time, Azure Analysis Services was a clear choice, but it is almost time to update that post with a longer article. Because of time constraints, I just want to quickly review what changed so far, promising a longer and more detailed update in a few months.

Read on to see Marco’s synopsis of what has happened since then. For my money, Power BI Premium Per User is already at a place where I’d prefer it to Azure Analysis Services.

Comments closed

Loading Data into Power BI Premium Per User vs Azure Analysis Services

Gilbert Quevauvilliers continues a series on moving from Azure Analysis Services to Power BI Premium Per User:

I have been working with a customer where I have got data in AAS and in PPU for the same dataset.

What I have found is that when the data is loading it is very similar in terms of how long the data takes to load.

With one of my customers as an example the data was being curated in Asia, whilst the business was running things from Australia. By hosting AAS/PPU where the data was curated meant that the data loading was significantly faster. Yes while the reports would have to access the data across the ocean, this only sends the results, so the performance of the reports was and is still blazingly fast!

Click through for the full story.

Comments closed

Comparing Azure Analysis Services Scaling to Power BI PPU

Gilbert Quevauvilliers continues a series on migrating from Azure Analysis Services to Power BI Premium Per User:

If you missed the first part of the series here is the link here: Query Performance – Part 1 Migrating Azure Analysis Services to Power BI Premium Per User – Reporting/Analytics Made easy with FourMoo and Power BI

In this blog post I am going to investigate how well does PPU scale when comparing it to AAS.

When comparing AAS to PPU, I must find the same size AAS size to what we get with PPU.

Read on for Gibert’s findings.

Comments closed

From Azure Analysis Services to Power BI Premium Per User

Gilbert Quevauvilliers picks back up on a new series:

Welcome to the first in my blog post series on evaluating the different aspects when looking to migrate from Azure Analysis Services (AAS) to Power BI Premium Per User (PPU).

Apologies for this taking a few extra weeks to get started, life has been super busy, but as they say “Better late than never”.

In this post I am going to compare the Query Performance of an AAS Cube compared to a PPU Cube.

Click through to see how Power BI Premium Per User stacks up against Azure Analysis Services.

Comments closed

Pre-Loading SSAS Databases into Memory Post-Restart

Nigel Foulkes-Nock explains why that first query after restarting SSAS can be slow:

When the SQL Server Analysis Services (SSAS) Tabular Service is started, it can take a long time before it is ready to be queried. This can cause delays to Service, not to mention confusion.

This Blog Post will explain what is happening during this time and a method that can be used to improve. It’s worth mentioning that the SSAS Tabular Databases that this has been used on are quite large (> 100Gb).

Click through for the answer, as well as a technique to warm up those servers so an end user doesn’t wind up being the one to pay for this wait.

Comments closed

From Azure Analysis Services to Power BI PPU

Gilbert Quevauvilliers teases a new series:

I have been doing a lot of evaluation and investigations for organizations who currently are using Azure Analysis Services (AAS) and looking to see if they can leverage Power BI Premium Per User (PPU)

In this series I am going to cover the following details below, which I completed to see if the migration was not only feasible but should be the new normal.

Looks like it will be an 11-parter, so we have some reading to look forward to.

Comments closed

Monitoring SSAS with Quest Spotlight

Slava Murygin has two questions and two answers:

This post is just answering two simple questions:

1. Can Quest Software’s Spotlight successfully monitor SQL Server Analysis Server?

2. If it can, what SSAS parameters, databases’ and cubes’ details it monitors and provides information about?

First, it’s good to see Slava back in the saddle again. Second, click through for those answers. Slava also promises to check out some other SSAS monitoring tools, so stay tuned.

Comments closed

Calculation Groups and Role-Playing Dimensions

Martin Schoombee takes a look at using calculation groups with role-playing dimensions:

The 2019 release of Analysis Services (compatibility level 1500) brought about a new feature called Calculation Groups, which makes it easier to apply the same logic to multiple measures without the need to duplicate code. Each calculation group represents an entity (table) with attributes (columns) and attribute values (calculation items), and because of this implementation it can be used to deal with role-playing dimensions as well.

Click through to see how it works, as well as some gotchas to keep in mind.

Comments closed