# Day: September 23, 2019

When it comes to customer feedback, you’ll find that not all the information you get is useful to your company. This feedback can be categorized into non-insightful and insightful data. The former refers to data you had already spotted as problematic, while insightful information either helps you confirm your hypotheses or notice new issues or opportunities.

Let’s imagine your company carries out a customer satisfaction survey, and 60% of the respondents claim that the pricing of your product or service is too high. You can use that valuable data to make a decision. That’s why this data is also called actionable insights because they either lead to action, validation, or rethinking of a specific strategy you have already implemented.

Survey design and implementation can be pretty difficult. This article does a good job pushing you away from some of the pitfalls around it.

The goal of a simple linear model is to fit a line onto this plot to summarize the shape of the data using the equation above.

The “a” value is the slope of the fitted line (rise over run) and the “b” value is the intercept on the y-axis (when x is equal to zero).

In the gapminder example, the life expectancy column was assigned as the “y” variable, as it is the outcome that we are interested in predicting or understanding. The year1950 column was assigned as the “x” variable, as it is what we are using to try and measure the change in life expectancy.

This is a little more complicated than adding a regression line to a scatterplot (the “normal” way to do linear regression with Power BI) but this method lets you work with the outputs in a way that the normal method doesn’t.

Take a system trained to make predictions on a language (word or character) model – an example you’re probably familiar with is Google Smart Compose. Now feed it a prefix such as “My social security number is “. Can you guess what happens next?

Read the whole thing. There’s a bit of discussion at the end around how you can stop this learning of secrets.

The keys we need for connecting the two tables are “region” and “certification”, respectively. A key on just one of these columns won’t ensure uniqueness and here is hurdle number one: how do we create a relationship in Power BI that is based on more than one key? Simple answer is that we can’t. But what we CAN do is create a concatenated column with the data we need to create a unique key, and then do our relationships based on that.

Click through for the process.

Again, focusing on the areas highlighted in orange: the statement has a parameter @0 (previously it had @1) but, more importantly, the clustered index is scanned now instead of the filtered index. This has impacts throughout the plan, including how many rows are both estimated to be read and actually read in order to return those 11 rows. You can see a much higher I/O cost (about 22X), the predicate is now listed explicitly in the tooltip, and you can see warnings about residual I/O (which just means a lot more rows were read than necessary). The root operator still has the warning about the unmatched index, so at least the plan gives you some clue that a filtered index exists that might be useful if you change the parameterization setting for the database (or add OPTION (RECOMPILE) to the statement):

There are still ways to make filtered indexes work with forced parameterization, such as index hints, but Aaron does a great job explaining why something which seems like it should just work doesn’t always.

Whilst playing around with my session for Techorama.nl I encountered an error I hadn’t seen previously whilst deploying SQL Server on Linux in Azure Kubernetes Service (AKS)

0/1 nodes are available: 1 node(s) exceed max volume count

The yaml I used was only slightly modified (mainly names) from scripts used on minikube and docker-desktop so I was a little confused as to why I was getting this in AKS.

Read on to understand what’s happening here and how you can fix it.

The main goal behind a graph design is to help you answer queries – so what are the questions you’d ask of a movie database, if you had one? Mine would typically be like below.

1 Who are the actors in this movie?
2 Who is this movie directed by?
3 Who is the most prolific actor, according this dataset?
4 How many actors are also directors?
..and so on.

Read on to see how you can write these queries.

1) Requires `ALTER` permission on the database. This is an elevated privilege – and may break the organisations sql server security policy