Getting my Twitter Data via Azure Logic Apps

As a mini project I wanted to use Azure logic apps to pull tweets from my twitter account when people were tweeting about #Azure / #Microsoft. From here I used cognitive services – sentiment analysis API which returns a numeric score between 0 and 1. Scores close to 1 indicate a positive sentiment and scores close to 0 indicate a negative sentiment. Then I wanted to put that data into an Azure SQL Database table and link it to Power BI because I wanted to see where in the world tweets were coming from and with what score.

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Checking your Wait Stats via Power BI

Wait stats is my go to thing, however I do get bored just querying it via a  table so I decided to broaden my horizons and see how “analytical” I could get with it via Power BI.

What I usually do is that I create a table and dump the contents of a famous waits stats script (*cough * Paul Randal, his code- NOT mine and all I do is put a timestamp on which I really want for time based analysis.

From there I can usually write some queries to see a trend – that is if one does exist.

Something like:

select * from dbo.WaitQueues
where waittype ='IO_COMPLETION' order by SampleTime


Anyways, I want to see Power BI in use now – first you need to download the Desktop version and install it. From




Select Get Data to setup a connection to your table.


You should then be able to preview your data.



Once the data has been loaded you can get creative – I am not creative at all! So if you are a Power BI person I apologise in advance.

The first pie chart shows the Summary of my waits at a specific time.


This is the pie chart at a different time point.


You can drill into a section of the pie chart to return the underlying details.


If you do not like using pie-charts you could switch to a tree map. This was me using CHECKDB heavily.


I wanted to see some visualization of what has happened to a specific wait over my time intervals.

For the below I selected WRITELOG over my time samples.





What about CXPACKET analysis?


So what on earth happened at 930? Yes that was me going crazy with poor performing queries on a poor performing disk!

For the next example I decided to use a stacked bar chart looking at a selection of wait types only at a specific time interval.  (9.08am)


That light blue (I think) colour seems to take most of the chart, that being LCK_M_S – you can then drill into it for more details.


(Yes that is a link to the awesome sqlskills waits library, yep I helped gather some data for that)

Or if you prefer a standard bar chart can be used, below concentrates on Wait (S) per wait type encountered.


Or changing the filters you could go by percentage per wait type.


Finally I get slightly more creative with signal wait times (red).


I am really enjoying this sort of analysis; it is a nice change from writing queries. Give it ago!