How to Create an Empty Extract in Tableau

Creating a chart or dashboard in Tableau without any data sounds a bit backward, but it's a powerful and practical technique for developers and analysts. The key to this process is the "empty extract," which lets you build the entire structure of a report - including all the worksheets, calculated fields, and formatting - before the final data source is even available. This article will walk you through exactly why and how to create and use an empty extract in Tableau.

Why Would You Need an Empty Tableau Extract?

Before diving into the steps, it's essential to understand the situations where this technique is a lifesaver. Building an empty extract isn't an everyday task, but when you need it, it's invaluable. It allows you to build a workbook's structure first and populate it with data later.

Here are the most common use cases:

• Building Dashboards Before Data is Ready: This is the most common reason. Imagine your engineering team is still building the data pipeline for a new product feature. You don't have to wait for them to finish. You can get the column names and data types from them, build the entire dashboard with an empty extract, and then simply connect it to the real data source once it's live.
• Creating Reusable Workbook Templates: If you're a consultant or part of a business intelligence team, you might need to create standardized dashboards for different clients or departments. You can build a master template workbook with an empty extract. Then, you can hand off the .twbx file to the end-user, who can easily connect it to their own specific data source to populate the pre-built visuals.
• Improving Development Performance with Large Datasets: Querying a data source with hundreds of millions of rows can be sluggish. Every time you drag a field onto the canvas, you have to wait for Tableau to process the request. By starting with an empty extract, you can build your charts and calculations instantly without any performance lag. Once the workbook's design is complete, you can point it to the large dataset and run a single, final refresh.
• Training and Certification: Understanding how extracts and data sources work in this way is a core competency for advanced Tableau users. Certain Tableau certification exams may even include scenarios that require you to understand or apply this concept.

The Step-by-Step Guide to Creating an Empty Extract

The entire process revolves around a simple idea: you'll create a tiny, temporary data file, generate a Tableau Extract (.hyper file) from it, and then immediately remove the data from that extract, leaving behind only the metadata (the column names and data types).

Step 1: Create a Dummy Data Source File

Your first step is to create a small file that contains the exact column headers and data types that your final, real data source will have. The easiest way to do this is with a simple Excel or CSV file.

For this to work smoothly, the following details are crucial:

• The column names in your dummy file must perfectly match the column names in your final data source. Case sensitivity matters: "Region" is not the same as "region".
• The format of the data should reflect the actual data type (e.g., use a date format for dates, numbers for sales figures, and text for categories).

Here's the trick: you only need one single row of sample data. Tableau uses this first row to infer the data type for each column.

For example, let's say our final dataset will contain sales information. We can create an Excel sheet named dummy_sales_data.xlsx that looks like this:

Date, Region, ProductCategory, SalesAmount, UnitsSold 1/1/2024, North, Gadgets, 150.75, 5

That's it. One header row and one row of data are all you need at this stage.

Step 2: Connect Tableau to Your Dummy File

Now, open Tableau Desktop and connect it to the placeholder file you just created.

1. On the Tableau start screen, under "Connect," choose the appropriate file type. In our example, we'd select Microsoft Excel.
2. Navigate to your saved file (e.g., dummy_sales_data.xlsx) and open it.
3. You'll be taken to the Data Source page in Tableau, where you should see your fields listed: Date, Region, ProductCategory, etc. Verify that Tableau has correctly identified the data types (e.g., a calendar icon for Date, a # symbol for numerical fields, Abc for strings).

Step 3: Generate the Initial Tableau Extract

With your dummy data source connected, the next step is to create a Tableau Extract file, which will have the .hyper extension.

1. On the Data Source page, in the top right corner, change the connection type from Live to Extract.
2. After selecting 'Extract', a link to 'Edit' appears. This lets you add filters or aggregate the data, but for an empty extract, we ignore these options.
3. Navigate to a worksheet (e.g., "Sheet 1"). Because you chose 'Extract', Tableau will now prompt you to save the .hyper file.
4. Give your extract a meaningful name, such as Sales_Dashboard.hyper, and save it in your project folder.

At this moment, you have a physical .hyper extract file on your computer that contains the structure and that one row of data from your dummy file.

Step 4: Remove the Data to Create the Empty Extract

This is the most critical step in the process, where you effectively "empty" the extract, leaving just the field definitions behind.

1. In the Tableau worksheet view, look at the Data pane in the top-left corner. You will see your data source listed.
2. Right-click on the data source name (e.g., dummy_sales_data).
3. From the context menu, hover over Extract.
4. In the sub-menu, click on Remove...

Tableau will present a dialog box with two options. Pay close attention here, as choosing the wrong one will undo your work.

• Option 1: Remove extract and switch to Live connection: DO NOT choose this. This would delete the .hyper file and revert to a live connection to your dummy Excel sheet.
• Option 2: Remove data from the extract, but keep the extract file on disk for future use: THIS IS THE CORRECT OPTION. Click this radio button.

Then, click OK. After you do this, something interesting happens. You still see all your fields in the Data pane, but now they are grayed out. This indicates that the fields exist in the extract's structure, but there are no values associated with them.

You can verify that this worked by right-clicking the data source again, navigating to Extract > Properties..., and checking that the "Number of Rows" is listed as 0.

Congratulations! You now have a true empty extract.

Working With and Populating Your Empty Extract

Your workbook is now a blank canvas, but with a predefined data structure. You can build your entire dashboard without seeing a single data point.

Building the Template

You can now drag and drop the grayed-out fields from the Data pane onto the Rows, Columns, and Marks cards just as you normally would. The view will remain blank, but the framework of your charts will be in place.

What you can do at this stage:

• Create any number of worksheets and position sheets on a dashboard.
• Build calculated fields using your placeholder field names (e.g., SUM([SalesAmount]) / SUM([UnitsSold])) to create an 'Average Price' calculation.
• Create and configure parameters and filters.
• Format tooltips, labels, titles, and axes.
• Set colors, shapes, and sizes on the Marks card.

You are essentially building a fully functional and cosmetically complete dashboard that is waiting for data.

Connecting to the Real Data Source

Once your real data is finally ready (e.g., the database table is now live), you can bring your dashboard to life.

1. Right-click the data source in the Data pane.
2. Go to Extract > Refresh (Full).
3. Tableau may ask you to locate the data file. Instead of pointing it to your old dummy Excel file, navigate to and select the final, real data source.

If your column names and data types in the real source perfectly match what you defined in your dummy file, Tableau will populate the extract. All of your pre-built charts, calculations, and dashboards will instantly fill with live, interactive data.

Common Pitfalls and Troubleshooting Tips

• Mismatched Names or Data Types: This is the number one cause of failure when refreshing. If the refresh fails, your first step is to meticulously check that every column in your real data source matches the dummy file - name, casing, and data type must be identical.
• Accidentally Switching to a Live Connection: If you accidentally select "Remove extract and switch to Live" in Step 4, your fields won't be grayed out. If this happens, you have to go back to the data source page and switch the connection back to 'Extract' to regenerate the .hyper file, then repeat the "Remove" step correctly.
• Forgetting to Save as a .twbx for Sharing: If you're building a template for someone else, be sure to save the final workbook as a Tableau Packaged Workbook (.twbx). This bundles the empty .hyper extract file with the workbook itself, so the recipient has everything they need.

Final Thoughts

Creating an empty extract is a technique that separates intermediate Tableau users from advanced ones. It streamlines development workflows, allows for parallel tracks of work between data engineers and analysts, and is the key to creating scalable, reusable dashboard templates. It's a prime example of thinking strategically about how you build reports.

While this is a powerful process within traditional BI tools, it also shows the level of manual setup often required to get started before you can even analyze data. At Graphed our goal is to eliminate this friction entirely. Instead of creating placeholder files, we let you connect your live marketing and sales data sources like Google Analytics, Shopify, and Salesforce in just a few clicks. You can start asking questions in plain English - like "create a dashboard of my sales by region for this quarter" - and get beautiful, real-time dashboards generated for you in seconds without any manual builds.