Last week’s post showed an M function that took Power Query diagnostics data and formatted in a way that made it suitable for visualisation in a Power BI Decomposition Tree visual. This is great for understanding what’s going on at a high level, but by doing this you also lose a lot of detailed information from the diagnostics logs that could be useful for performance tuning.
Performance wise it can be very interesting to use query objects as a data-source in a report (or any other object).
Imagine you want to create a report that shows the top X records from a table, let’s say for example Customers, then creating a report dataset with a top-x filtering can be very cumbersome.
Azure DevOps is a very handy tool to manage project tasks, milestones, bugs, and documentation.
But it is not just limited to that, it can also be used to manage all your deployments and building pipelines to manage your deployments.
Lets take a look how to do this and how we can setup our repository to make auto deployments.
Imagine you have created an amazing Power BI report that summarizes information using different visuals. Power BI Desktop lets you build advanced queries, models, and reports that visualize data and the idea is that the individual records that come from your data source are aggregated and summarized in your report, giving you the possibility to see the big picture.
Microsoft in late January and early February published a series of pages on Microsoft Docs dedicated to performance with Business Central (On-premise, Cloud).
They are all very interesting, in particular, I want to report this page because it is very interesting for functional consultants (as in my case) more than for technicians; these are the things we repeat daily to our customers …and finally Microsoft has decided to formalize them.
Today I was helping a customer with a problem that seemed quite simple on the surface. She had a data table containing historical customer sales orders (each customer has many orders on different dates). The objective was to filter this table in Power Query and just load one record for each customer – the one that was the last order date. To illustrate the problem more clearly, I have adapted the scenario using the Adventure Works database.