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Author: Kevin Feasel

Asking The Right Question

Buck Woody argues that the hardest thing about data science is asking the right question:

When I started down the path of learning Data Science, I was nervous. I have to work hard at math – it’s a skill I love but one that does not come naturally to me. I was nervous because I thought the most daunting task I would face in Data Science waslearning all the algebra, statistics, and other maths I would need to do the job.

But I was wrong.

Math isn’t the hardest thing in Data Science. Actually, since it’s so mature, and documented, and well-known, it’s quite possibly the easiest thing to conquer in the skillset. No, the hardest thing about Data Science is asking the right question.

I’ll lodge a bit of a disagreement here.  I’m okay with the argument that asking the right question is the toughest part, but the math’s not particularly easy either…  Knowing when to use which distribution, which model, and which parameters requires a definite amount of skill.

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Multi-Tab Reports With R And jQuery

Matt Parker shows us how to create multi-tab reports using jQuery UI and R data:

But R is also part of an entire ecosystem of open tools that can be linked together. For example, Markdown, Pandoc, and knitr combine to make R an incredible tool for dynamic reporting and reproducible research. If your chosen output format is HTML, you’ve linked into yet another open ecosystem with countless further extensions.

One of those extensions – and the focus of this post – is jQuery UI. jQuery UI makes a set of JavaScript’s most useful moves available to developers as a robust, easy-to-implement toolkit ideal for adding a bit of interactivity to your knitr reports.

Generating a page from R is one of those good ideas that I probably don’t want to see in a production environment.

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Conformity In Self-Service BI

Paul Turley has a nice post on some of the risks of self-service BI:

In some solutions with a manageable scale and a reasonable tolerance for a certain amount of data loss and inconsistency, this approach may be just fine.  There are very good reasons for inconsistencies between sets of data that come from different sources.  When 20 million fact rows are obtained from an online ordering system and .1% don’t have matching customers records that come from the system used to manage the master customer list, it may simply be a question of timing.  Or, maybe the definition of “customer” is slightly different in the two systems.  Chances are that there are also legitimate data quality issues affecting a small percentage of these records.

Whatever the case may be, a data conformity or potential data quality issue in this design scenario falls on the model designer to either fix or ignore.  Again, this may or may not be a concern but it is a decision point and might raise the question of ownership when questions about data quality are raised.

Paul then goes on to show how this gets fixed in a traditional model and where you need to watch out with SSAS Tabular.  Good essay worth reading.

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Visualizing R In Power BI (Too)

Dustin Ryan is also looking at R visualization in Power BI:

Not only can we create and download custom visuals from to extend the capabilities of Power BI, we can use R to create a ridiculous amount of powerful visualizations. If you can get the data into Power BI, you can use R to perform interesting statistical analysis and create some pretty cool, interactive visuals.

Dustin and Jan Mulkens are working on similar posts at the same time, so watch both of them.

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Trace Flags Without Sysadmin

Jack Li shows how to enable a trace flag without sysadmin or changing any application code:

The initial thought is to enable the trace flag at session level.  We ran into two challenges.  First, application needs code change (which they couldn’t do) to enable it.  Secondly, dbcc traceon requires sysadmin rights.   Customer’s application used a non-sysadmin user.  These two restrictions made it seem impossible to use the trace flag.

However, we eventually came up with a way of using logon trigger coupled with wrapping the dbcc traceon command inside a stored procedure.   In doing so, we solved all problems.  We were able to isolate the trace flag just to that application without requiring sysadmin login.

This is the very edge of an edge case.  In normal practice, change the code.

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Trustworthy Databases

Kenneth Fisher asks if you check TRUSTWORTHY settings on your databases:

I wasn’t surprised (although a little disappointed) that out of the 9 people the answered only one person was, and of the rest 5 didn’t even know what TRUSTWORTHY is. I even had one person ask me later. That’s somewhat scary because under the right circumstances if you give me a database with TRUSTWORTHY turned on I can take over your instance. I’m NOT going to show you how but it isn’t terribly difficult.

I’ll admit that I have been a bit non-chalant about TRUSTWORTHY in the past, but turning it on is the smart move.

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Bad Fixes

David Alcock looks at a few common “fixes” which end up causing their own problems:

I’m seeing lots of CXPACKETS waits, how do I fix these?

Bad Advice = Set the maximum degree of parallelism to 1, no more CXPACKET waits!

I’m seeing index fragmentation occur quite frequently on some of my indexes, what should I do?

Bad Advice = Set fill factor on the server to 70, no more fragmentation problems!

I’m worried about TempDB contention. What should I do?

Bad Advice = Set the number of files to the number of cores, no more contention issues!

Read the post for better advice.

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Power BI And R

Jan Mulkens has started a series on combining Power BI and R.

Part 1:

Fact is, R is here to stay. Even Microsoft has integrated R with SQL Server 2016 and it has made R scripting possible in it’s great Azure Machine Learning service.
So it was only a matter of time before we were going to see R integrated in Power BI.

Part 2:

From the previous point, it seems that R is just running in the background and that most of the functionality can be used.

Testing some basic functionality like importing and transforming data in the R visual worked fine.
I haven’t tried any predictive modelling yet but I assume that will just work as well.

Part 3:

So instead of printing “Hello world” to the screen, we’ll use a simple graph to say hello to the world.

First we need some data, Power BI enables us to enter some data in a familiar Excel style.
Just select “Enter Data” and start bashing out some data.

I’m looking forward to the rest of the series.

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