![]() ![]() Share some of your exciting data combinations on Tableau Public! I’ve had a ton of fun mashing up internal data with Tableau 10. With Tableau 10 and cross-database joins, you can bring together all of your data, explore it in new light, and uncover new insights with just drag and drop. In the snapshot, I’ve highlighted Tableau’s home turf, the Fremont neighborhood, and we can see that it has a very healthy number of four-star ratings! Then I’ll make another viz showing the distribution of rating reviews by zip code: I’ll go back to the viz window and make a quick map showing zip codes colored by the average price versus average rating: Note that the new data columns are colored green: I’ll just drag-and-drop the text file into my data source. Maybe I want to bring in the regional home-sales information as well. You can add as many of the supported connections as your analysis requires. ![]() You can see that I have blue columns from SQL Server and orange columns from MySQL that appear side by side in the data grid! Now you can write row-level calculations using fields from both databases, or even create an extract of this multi-connection data source.Īnd you’re not restricted to just two connections. We color-code each connection so you can distinguish the tables in the join and the columns in the data grid. Now I can just drag the MySQL “reviews” table into the canvas to join: I’ll double-click to rename the connection: Once the connection is added, you’ll see it appear in the top-left part of the data tab. I’ll click on MySQL and enter my connection information. Specifically, you cannot use cross-database joins with these connection types: That’s because they aren’t supported for cross-database joins yet. You’ll notice that a number of the connection types are grayed out. With cross-database joins, I can now simply add MySQL to the data source.īack on the data tab, click the “add” link to add other connections to this data source. With this data set, I can quickly build vizzes which show all the listings in Seattle, sized and colored by nightly rates:īut what if I want to combine this with my Rental Review data set, which is in MySQL? Before Tableau 10, I could use blending, but then I wouldn’t be able to generate extracts, publish the data source, or use aggregations like MEDIAN(). Let me show you why this feature can be so powerful.įirst, I’ll connect to a database on SQL Server to access Seattle overnight-rental data. That’s why I am so excited about cross-database joins, a new feature in Tableau 10. Sometimes, to answer your hardest questions, you have to integrate multiple data sets to uncover insight. You can't always answer your questions with a single data set. Update: Tableau 10 is here! Download now to try out the feature outlined below.
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