Union tableau prep6/7/2023 In Alteryx, you build a flow, usually starting with one or more Input Data tools to bring in data from a source such as a text file or database and then, using various other tools like joins, formulas, unions, and filters, you can shape the data with a lot of flexibility. Alteryx Data TipsĪlteryx offers a slightly different paradigm for bringing various data sources together for analysis. It takes the concepts that are already built into Tableau Desktop to a whole new level to make data preparation and shaping even easier and more robust. And with various other data prep tools such as pivot, merge and join calculations, you have tremendous power to shape the data in just the right way for your analysis.Īlso, be on the lookout for a new product from Tableau - code-named “Maestro” -which was demoed at Tableau Conference 2016. No problem! Tableau will let you bring together all of these and more. Do you have customer data stored in a couple of SQL Server and Oracle databases? Do you have supplemental data in Excel and a Google Sheets document that the team updates every day? Tableau allows you to join tables from various data sources together to create a single data source that will meet your analytical needs. You can get very creative and do things like change the level of aggregation on the fly via parameter or blend between different time periods for period-over-period comparisons.ģ. Since data blending happens at the same time as data visualization, it’s less of a data preparation step and more of a “real-time” experience with the data. ![]() Does one of your data sets contain a record for every customer for every day and another contain monthly goals? If you joined those two sets you might end up with a lot of duplication of monthly values, but when you use data blending, your monthly values come through perfectly. This can improve performance and give you quite a bit of flexibility with a data model. Instead of joining them together, use Tableau to blend the data at an aggregate level. ![]() This can be very useful in an enterprise data warehouse where you have large fact tables.
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