My grandparents have a beach house in Island County and every 4th of July there is a big parade and community get together. One of the events is the “Penny Hunt”. The adults scatter a bunch of coins (of varying denominations) in the sand for the kids to search for. As kids, my brother and I got fed up with blindly digging in the sand so we convinced our dad to get us a cheap metal detector. I remember pulling in $40 the first summer we put it to use. Not bad for a couple of kids.
If you need to pivot data from a SQL table for use in Tableau, Tableau Desktop’s default pivot feature can’t help you.
You might never have heard of a self join but you might need one. Occasionally, tables in a database are structured so that it makes sense to join a copy of a table to itself…
Sometimes you need to use data in Tableau that isn’t in a clean, denormalized format. It might have been exported from an application or prepared by a coworker in a way that Tableau doesn’t like.
I frequently hear the question, “Can Tableau show my missing data?”. Generally when I get this question, people want to either see a 0 or a blank where there should be missing data.
Have you ever found that Tableau Desktop took a long time to load a worksheet or apply a filter? You might have found yourself wondering “Is my data source too large for Tableau?” The answer is…“maybe”.
If you’ve been using Tableau Desktop for a while you probably know that you can join, union and pivot data in the product. When you hear that Tableau Prep helps you “prepare your data” you might wonder what it can do that Desktop can’t.
Tableau Desktop will allow you to union multiple tables from the same database or even multiple .csv files, but you can’t union a table from SQL Server A with a tableau from SQL Server B.
Tableau Prep is a powerful tool but it can’t help solve every data preparation scenario. We focus a lot of our time and effort on what it can do, but we thought it would be worthwhile to cover what it can’t do (yet).
Joins can be a sticky business, especially if…
● You haven’t used them much before.
● You are working with data that is new to you.
● You don’t trust your data cleanliness.