![]() For example, you might analyze student test scores in your district and want to see how socioeconomic statuses play a role, so you integrate census data. Ask yourself: Is the data in a single table or in multiple tables in a single database? You may need to combine multiple databases to get to the root of your questions-or if you need a more robust view, you may need to bring in an external data source. Once you can access the necessary data, you'll need to determine where it all lives. To see more about how data should be structured for analysis in Tableau Desktop, visit the online help page. For example, do you even have the right permissions to access a given data source and is it in the right shape? In other words, when you bring it into Tableau, can you do the analysis you want? You’ll need to address both of these questions before you even start the preparation process. There are some basic considerations when it comes to this question. Reference Materials Toggle sub-navigation.Teams and Organizations Toggle sub-navigation.I hope this tutorial helps you solve your own challenges bringing multiple database into Tableau. Look out for a future blog post covering this topic. To accomplish this, you need a row for every day you want to visualize, so you would join into a date dimension or scaffold table. I’ve run into this challenge creating resource planning dashboards where the project manager wants to apply a man-hour curve to a project timeline. Imagine you only had start and end dates for an event, but wanted to distribute a measure across those dates. The ‘scaffolding’ technique can also help create a continuous date field or axis if your original data source doesn’t have one. I unfortunately found this out the hard way, but hopefully you won’t with this guidance. You must create all joins first, and then go back and union within your established data sources. If you union tables within your data sources and then join like I did, you’ll also need to follow a very specific order or Tableau will throw an error. The ‘Organize by Folder’ option is a great way to store fields in an orderly manner. I also like to create naming conventions and acronyms for my fields to easily swap them in and out of calculations. ![]() Keep in mind that your data sets have a lot of columns, then you must create a lot of calculated fields, potentially impacting performance. Additional Considerations and TipsĬross-database unions aren’t pretty, but they get the job done. There we go! We have successfully unioned data from two different databases. I split the data set into two files, one with data from East and West regions and the other with data from Central and South. To explain how this works in detail, I will use Sample Superstore data. Tutorial: Step by Step Cross-Database Union in Tableau We only had to consolidate measures for this project. We were also used the BU and Date fields from the forecast data source without consolidating since they existed on every row. ![]() I only included one metric for simplicity. Luckily, this was a high-level dashboard, so we structured the custom SQL queries to give us only three rows of data per day (one per BU), so performance was not a factor.īelow is a simplified example of what one day of records looks like in the final data source. We ended up with 193 columns total after creating all calculations. Each data source had between five and ten original columns, depending on what metrics were involved, for a total of 48 columns. This consolidation process proved extra tricky because we needed to weight measures on a daily level and also perform ratio calculations between them. The next step was creating consolidated fields for the dashboard, because the cross join produces null values in a third of the cells. This cross join gave us all needed rows in one data source.
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