I’ve been a bit quiet lately, but Tableau Prep out the door and it’s time to make a little noise.
Clark recently wrote an excellent post on the basic UX architecture of Prep. Here I’d like to cover a key concept underlying Prep that may be a bit foreign to people coming from Tableau: the flow.
This isn’t the most glamorous part of Prep, but it is one of the most fundamental concepts in the tool, so it seems worth spending some quality time on.
Strap on your life jacket and read on for more.
When using Tableau, taking an extract is always better than using a live query, right?
Of course. Obviously, when your data are changing and you want to get all of the latest updates in your viz, you’ll want to use a live query. But if that’s not the case, then an extract is clearly better, especially with Hyper in 10.5, right?
Shoot! This is complicated? When will live beat an extract? Let’s take a look at a few cases.
Not too long ago, I bought a telescope. I guess that makes me an amateur astronomer.
If you buy a camera, you are a photographer. If you buy a flute, you own a flute.
– Bob Kolbrener
So maybe I just own a telescope.
I certainly need some help when it comes to choosing things like eyepieces, so I was thrilled to come upon a very thorough list of eyepieces assembled by Starman1 (Don) over at Cloudy Nights.
But a spreadsheet is one thing—a viz is better. My take at an explorative viz is online over at Tableau Public. (I wish I could figure out how to embed something here, but all I can manage is a screenshot.)
Go ahead and tweak the parameters to find the eyepiece you’re looking for. Some details on how I built it are below the fold. Continue reading
Several posts here have explored the queries Tableau generates as it builds your viz, including last week’s write-up on custom SQL. This is a trend that will continue: it’s much easier to understand a machine when you can see its inner workings.
But how do I get at those queries? I was talking with Yvan Fornes, and he suggested that I write about how I do it.
Challenge accepted! Except I may have gone overboard: in this post I’ll explore three ways to find the queries underlying your viz.
I recently answered a question on the Tableau Community forums that arose from confusion over why some (perfectly correct) SQL wasn’t working as custom SQL in Tableau. The poster wanted a list of Tableau’s supported syntax.
But as it turns out, that’s the wrong question: Tableau doesn’t have a list of all the custom SQL syntax it supports because it really is just passing along the SQL code as you’ve typed it.
So why would a perfectly reasonable custom query fail? And what’s the link to SQL injection? Read on!
Continuing last-week’s trend, we’ll again take a look at an aspect of Tableau that people often find confusing: the difference between live and extracted data sources. And again, we’re going to take a bit of a database perspective to clarify the situation.
The impetus for this post is a number of statements I’ve seen along the lines of:
A live data source is just a real-time extract of your data.
This is my favorite kind of wrong: subtly wrong.
I thought I’d kick this off gently. I remember going through Boot Camp after joining Tableau and learning about dimensions and measures. And I remember finding the descriptions rather confusing.
I don’t recall the precise phrasing, but it went something like this:
Dimensions are usually those fields that cannot be aggregated; measures, as its [sic] name suggests, are those fields that can be measured, aggregated, or used for mathematical operations.
Measures are the result of a business process event… Dimensions are reference variables that give context to measures.
I don’t really mean to criticize these definitions, but to a database guy, they seem rather imprecise. For someone with a little SQL know-how, the actual definition is both crisp and helpful in understanding what Tableau really does under the covers—this helps predict what actions in the UI will do, so you don’t just blindly drag-and-drop until things look right.
The rest of this post is a crisp explanation of dimensions and measures for someone who knows a little SQL.