![]() ![]() ![]() In all cases, the number of rows is capped at roughly 1 million. We can use a heuristic to describe its behavior. A badge is displayed when sampling is applied.Īs you may have guessed, the algorithm that determines the maximum number of rows is rather complex. If that’s the case, then sampling won’t be applied. Often the maximum number of rows is greater than the number of rows in your data set. The algorithm used to determine the sample size calculates the maximum number of rows based on the number of columns in the Input step and their respective data types. ![]() When you run your flow, changes are always made to the entire data set-and not to a sample-so you can walk away with a clean, ready-to-analyze data set. Samplingīy default, while authoring flows, Prep automatically applies sampling to limit the amount of data it processes. These improvements help you quickly remove columns and easily filter to the time period required for your analytics. You can also add no-code relative date filters for DateTime data types in the Input step. With the Tableau Prep 2023.1 release, you can bulk select and remove multiple columns. Learn more about how Prep works under the hood in this blog post. Performing the actions after the Input step, say within the Clean step, won’t provide the same benefit. These actions guarantee that unnecessary data won’t be loaded into memory while authoring your Prep flow and will limit the amount of data queried when you run your Prep flow. You can help Prep run faster by removing columns and filtering out data that isn’t essential to your workflow in the Input step. In this example, the SQL query took over 38 minutes to complete in the native database portal. Doing so can significantly increase the time it takes to load your data and run your flow. This database table-dating back to 2019-contains a whopping 14.5 billion records! Oftentimes, analyzing older data (in this case, data from five years ago) isn’t necessary. Let’s look at a real-world example of a Tableau data set. A simple yet powerful way to minimize the time needed for Prep to load your data and run your flow is to only work with the data you need. The more data you bring into your data preparation flow, the more computationally expensive it will be. Give these tips a try and let us know what you think. These tips can be used in any of your Prep flows but will have the most impact on your flows that connect to large database tables. In this blog, we’ll discuss ways to make your data preparation flow run faster. With Prep, users can easily and quickly combine, shape, and clean data for analysis with just a few clicks. Tableau Prep is a citizen data preparation tool that brings analytics to anyone, anywhere. Reference Materials Toggle sub-navigation.Teams and Organizations Toggle sub-navigation. ![]()
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