Imagine you have some kind of system that produces reports on your data – for this example I randomly decided to use bookings for events -, and these reports are published on a regular schedule. Now you want to see two things in your report:
- The current status of participants per event – both for past events (i.e. the actual number of participants) and for future events (i.e. the current number of people registered).
- An overview of how the number of people registered changed over time.
Also, your source system is publishing these data as
.csv files. How can this be done?
Well, very easily using the new wildcard union feature introduced in Tableau Desktop 10.1! Read on to see how this can be done.
Continue reading →
This was the title of an invited talk I gave at MongoDB’s first public event in Germany on September 26th. MongoDB is awesome in that it is able to handle large amounts of both structured (read: relational sources) and unstructured (read: NoSQL) data. Also, the ability to integrate data from a number of disparate sources and the fast response times make it a good fit to be used together with Tableau for any kind of ad-hoc analysis task. In order to show these capabilities and also to have some fun I decided to spice up the introduction of Tableau I provided there with a little live demo of how this looks in real life. When it came to select what data to use I decided to go with movie data – a logical choice since we have the Tableau Cinema Tour coming up soon (see below). Also, one of our founding fathers here at Tableau is Prof. Pat Hanrahan, who received his first Academy Award (of three!) for the development of the RenderMan® Software that only made movies like Toy Story possible in the first place. Continue reading →