![]() Often, your data only needs high-level restructuring, which doesn’t require insight into every individual row of data. In this case, filtering on the deal year equal to 2022 in the Input step will force sampling to be applied only to those deals versus deals from every year. Let’s say, for example, that you’re only interested in deals closed in 2022. To further refine the sample, you can filter values in the Input step. ![]() If you’d like to get a more representative sample, you can change the sampling method to random in the Data Sample tab within the Input step. When sampling is applied, the top rows, sorted by source row order, are queried and appended to already cached rows. To increase the maximum number of rows, you can remove unnecessary columns in the Input step. If your data set has 16 columns, then the maximum number of rows is halved again, to 250,000, and so on. If your data set has 8 columns, then the maximum number of rows is halved to 500,000. For most data types, your data set can have up to 4 columns and still maintain the 1 million row limit. 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.
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