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Sometimes you may have daily, weekly, monthly or yearly data dumps where you want to only run the most recent file. Within Alteryx you can make this process dynamic and seamless through the use of a few tools.
One of the great features of the output tool is the option to take the file or table name (or part of it) from a field. It allows you to append a suffix, prepend a prefix, change the entire file name, or the entire file path. It also gives you the option whether to keep the field on output.
For most tools that already have “dynamic” in the name, it would be redundant to call them one of the most dynamic tools in the Designer. That’s not the case for Dynamic Input. With basic configuration, the Dynamic Input Tool allows you to specify a template (this can be a file or database table) and input any number of tables that match that template format (shape/schema) by reading in a list of other sources or modifying SQL queries. This is especially useful for periodic data sets, but the use of the tool goes far beyond its basic configuration. To aid in your data blending, we’ve gone ahead and cataloged a handful of uses that make the Dynamic Input Tool so versatile:
Here at Alteryx we believe in working smart, not hard. Building out reports to highlight business-critical metrics is a pretty smart way to track goals. Customizing those reports to everyone in the department, then distributing them as attachments to individual emails? That sounds like a lot of hard work. Scheduling those reports from a refreshing data source each month so you don’t have to remake or rerun the reports yourself - that’s genius. Logging into your work computer to open up Alteryx, then having to check the scheduled results before having any peace of mind those reports were delivered without a hitch? Hard.
The Dynamic Rename Tool is part of the developer category of tools. It allows the user to quickly rename any or all fields within an input stream by employing the use of different methods.\n \n The user has the option to rename only certain fields, all fields, or even dynamic/unknown fields at runtime (e.g. after a Cross Tab Tool). The option for renaming fields are:
The Multi-Field Formula Tool offers the same functionality as the Formula Tool, but offers the added benefit of applying a function across multiple fields of data all at once. Gone are the days of writing the same function for multiple fields.\n
Say there are four fields with dollar signs ($) that need to be removed. It could be done with a Formula Tool and a function written for each field:
In this article, we demonstrate how to import multiple worksheets from one Excel file, how to import multiple worksheets from multiple Excel files, and how to import a specific range of cells from an Excel sheet.
When you’re frequently writing and rewriting data to Excel spreadsheets that you use for Excel graphs and charts, it can quickly become a hassle to make and remake your reporting objects to keep them up-to-date so you’re visualizing the most recent data. A best practice to keep the hassle out of the process exists, though! If you keep your plots isolated to their own spreadsheet, referencing cell values in another sheet used to capture your data, you can simply overwrite the source data sheet and your plots will update automatically upon launching Excel. In the example below (attached in the v10.6 workflow Dynamically Update Reporting from Excel Spreadsheets.yxzp) we’ve included the workaround to make your Excel outputs seamless.
Dynamic Input threw error: ParseError: Invalid document structure at line 1 and column 1 while processing "AliasFileXml" on the workflow with "Root element is missing." in a popup. Upon clicking OK, a different pop-up comes up saying "Internal error in SRC_GetCosmeticName_Raw"
Upon creating a BINGO game, I came across a technique that I thought could be useful in "real world" scenarios for users who are attempting to iterate a process and then replenishing the data after a certain amount of time.
One of the best things about Alteryx is the ability to read in multiple files very easily and automatically combine them into a single dataset. This becomes a bit trickier when dealing with files that have different schemas or Excel files with multiple tabs. Adding both multiple excel files with multiple tabs, and having the schema change within each tab takes it to another level.