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How to Create and Use Tableau Extracts Effectively

Tableau is a powerful data visualization tool that allows users to create interactive and insightful visualizations. One of the key features of Tableau is the ability to create and use extracts, which are subsets of data that are optimized for performance. In this article, we will explore how to create and use Tableau extracts effectively, and discuss the benefits and best practices associated with using extracts.

What are Tableau Extracts?

Tableau extracts are subsets of data that are stored in a highly optimized format, allowing for faster performance and improved data analysis. When you connect to a data source in Tableau, you have the option to create an extract of the data, which can be used to build visualizations and perform analysis.

Tableau extracts are created by selecting specific dimensions and measures from the data source, and then saving them as a separate file. These extracts can be refreshed to include new data or updated to reflect changes in the underlying data source.

Benefits of Using Tableau Extracts

There are several benefits to using Tableau extracts:

  • Improved Performance: Tableau extracts are optimized for performance, which means that queries and calculations can be executed faster compared to working directly with the entire data source. This is especially useful when working with large datasets or when performing complex calculations.
  • Offline Access: Tableau extracts can be saved locally, allowing users to work with the data even when they are not connected to the original data source. This is particularly useful for users who need to analyze data on the go or in locations with limited internet access.
  • Data Source Independence: Tableau extracts are independent of the original data source, which means that users can work with the data without worrying about changes or updates to the source. This is especially useful when the data source is frequently updated or when multiple users need to work with the data simultaneously.
  • Aggregation and Filtering: Tableau extracts allow users to aggregate and filter data at the extract level, which can improve performance and simplify analysis. By pre-aggregating data or applying filters, users can reduce the amount of data that needs to be processed, resulting in faster queries and calculations.
  • Data Security: Tableau extracts can be encrypted and password-protected, providing an additional layer of security for sensitive data. This is particularly important when sharing extracts with others or when working with confidential information.

Creating Tableau Extracts

Creating a Tableau extract is a straightforward process. Here are the steps to create an extract:

  1. Connect to the data source: In Tableau, go to the “Connect” pane and select the data source you want to extract.
  2. Select data to extract: In the “Data” pane, choose the dimensions and measures you want to include in the extract. You can also apply filters or aggregations at this stage.
  3. Create the extract: Once you have selected the desired data, go to the “Data” menu and choose “Extract Data.” Tableau will prompt you to specify a location and name for the extract file.
  4. Set extract options: In the extract creation dialog, you can specify additional options such as data filtering, aggregation, and sorting. These options can help optimize the extract for performance.
  5. Save the extract: After setting the extract options, click “Extract” to create and save the extract file.

Once the extract is created, you can use it to build visualizations and perform analysis in Tableau. The extract can be refreshed to include new data or updated to reflect changes in the underlying data source.

Using Tableau Extracts Effectively

While Tableau extracts offer many benefits, it is important to use them effectively to maximize their potential. Here are some best practices for using Tableau extracts:

1. Consider Data Size and Performance

When creating extracts, consider the size of the data and its impact on performance. Large datasets may require more memory and processing power, which can affect the performance of Tableau. It is important to strike a balance between the amount of data included in the extract and the performance requirements of your analysis.

Additionally, consider the frequency of data updates and the need for real-time analysis. If the data source is frequently updated and real-time analysis is required, using live connections instead of extracts may be more appropriate.

2. Apply Filters and Aggregations

Tableau extracts allow you to apply filters and aggregations at the extract level, which can improve performance and simplify analysis. By pre-filtering the data or aggregating it at a higher level, you can reduce the amount of data that needs to be processed, resulting in faster queries and calculations.

For example, if you are working with a sales dataset and only need to analyze data for a specific region or time period, you can apply filters to the extract to include only the relevant data. Similarly, if you are interested in analyzing sales by month rather than by day, you can aggregate the data at the month level to reduce the number of data points.

3. Optimize Extract Refreshes

When refreshing extracts, consider the frequency and timing of the refreshes to minimize disruption and maximize efficiency. If the data source is updated frequently, you may need to schedule regular extract refreshes to ensure that the extract reflects the latest data.

However, frequent refreshes can impact performance and put a strain on the data source. It is important to find the right balance between the frequency of refreshes and the performance requirements of your analysis.

4. Secure and Share Extracts

Tableau extracts can contain sensitive or confidential information, so it is important to secure them appropriately. Tableau provides options to encrypt extracts and password-protect them, ensuring that only authorized users can access the data.

When sharing extracts with others, consider the size of the extract file and the method of sharing. Large extract files may be difficult to share via email or other file-sharing methods. Tableau Server or Tableau Online can be used to securely share extracts with other users, providing a centralized and controlled environment for data access.

5. Monitor Extract Performance

Regularly monitor the performance of Tableau extracts to identify any issues or bottlenecks. Tableau provides tools and features to monitor extract performance, such as the “Performance Recording” and “Query Performance” tabs.

By monitoring extract performance, you can identify areas for improvement and optimize the extract for better performance. This may involve adjusting extract options, optimizing queries, or making changes to the underlying data source.


Tableau extracts are a powerful feature that allows users to create subsets of data for improved performance and analysis. By creating extracts, users can benefit from improved performance, offline access, data source independence, aggregation and filtering capabilities, and enhanced data security.

To create Tableau extracts effectively, it is important to consider the size of the data, apply filters and aggregations, optimize extract refreshes, secure and share extracts appropriately, and monitor extract performance.

By following these best practices, users can harness the full potential of Tableau extracts and unlock valuable insights from their data.

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