How To
Dec 11, 2023

How to Generate Fake Data for Testing & Development

Shipping new features without testing is like publishing a book without proofreading. It’ll create too much friction in your product experience, leaving users struggling with bugs and errors. 

Most product teams can easily solve this problem by testing new capabilities using fake data.

Wondering where you’ll find this fake data in bulk?  

Answer: with Gigasheet’s enrichment function. 

Never heard of Gigasheet? Well, it's big data spreadsheet that can handle up to a billion rows of data.

Now, let me show you how to generate fake data for testing.

Generating fake data for Testing with Gigasheet: A walkthrough 

Most fake data generators are complicated and require technical expertise. You need to understand coding to extract relevant information and use it for testing. 

Gigasheet is a simple, no-code alternative for all these fake data generation tools. 

The platform lets you enrich any dataset with dozens of third-party data providers, like, Apollo, BuiltIn, Clearbit, and more. It can also fetch data directly from ChatGPT and expand your data or create fake data. I’ll show you the latter use case with an example. 

I’m creating a fake database for testing a personal finance app. 

Let’s break down the steps I followed:

Step I: Open your Gigasheet account and create a new sheet

I logged into my Gigasheet account (you can make yours) and started fresh with a blank sheet. 

Upload/import spreadsheet file in Gigahsheet

Then, I added 10,000 names to this blank sheet. These are the fictional users for my financial planning app. The idea is to populate this fake database by generating more details about each user, like income, expenses, investments, etc. 

Gigasheet file editor

Step II: Open Enrichments and click on OpenAI

The next step is running a custom HTTP request to enrich my data. To do this, you have to click on the Enrichments button, and you'll be able to see all data sources compatible with Gigasheet.

At the very top, you’ll see an enrichment option for OpenAI - GPT for Gigasheet. Click on this to start an enrichment request. 

OpenAI - GPT for Gigasheet enrichment

Step III: Add your prompt and start a test

In the next step, you have to simply type in your prompt for ChatGPT to generate the data. You can also adjust the randomness scale to get well-defined or creative results. 

Here’s the prompt I entered to get 10,000 varied figures for income in US dollars. I also added an instruction to generate a list without numbers or bullet points. You can easily customize this part based on your exact requirements. 

Prompt for OpenAI - GPT for Gigasheet

Once you’re happy with the prompt, hit Test, and Gigasheet will give you a preview of the results. 

Step IV: Check preview data and press "Apply"

Based on my prompt, I received this data as the preview result. Since all these numbers were in even figures and looked similar, I clicked Back and tweaked my prompt to mix up the numbers. 

If you’re happy with the preview results, you can press Apply to move forward. 

Preview data inside OpenAI - GPT for Gigasheet

You'll now be prompted to confirm the API request. Gigasheet will show you the number of requests you're using in each run, and you can tweak the previous steps to increase/decrease the number of requests. 

When everything is finalized, hit Run to start enriching your data.

Confirm OpenAI - GPT for Gigasheet action

Step V: Edit and organize the enriched data

Here’s a glimpse of the data I generated through my ChatGPT enrichment request. 

Edit and organize the enriched data

I followed the same steps to generate more data for a new column called ‘expenses’ and additional columns for investments, savings, loans, etc. You can generate up to 10,000 data points or more based on your enrichment credits.  

You can generate up to 10,000 data points or more based on your enrichment credits.

Once I completed the enrichment steps, I was able to edit my dataset with several capabilities:

  • Filtering: Viewing specific data points that matched given conditions
  • Sorting: Arranging all data in an ascending or descending order
  • Grouping: Categorizing different users based on their income groups
  • IF/THEN: Expanding on my dataset by defining some conditions and rules 
  • Data cleanup: Replacing data points, combining/splitting columns, and more

Overall, I could generate a fake database for testing a financial app in under 10 minutes. Not only that, I was also able to customize this data and make it more relevant with Gigasheet's intuitive editing capabilities. 

You can also go a step beyond and let AI do all the heavy lifting on your behalf. Gigasheet's Sheet Assistant can simplify data analytics and processing with simple prompts. You only have to give it a command explaining what you want to do, and the Sheet Assistant will perform the action and then explain all the steps to you. 

Gigasheet AI assistant

Generate rich mock data for testing in minutes! 

You don't have to spend hours figuring out how to make things work in a complex data generation tool. Gigasheet requires almost no learning curve because it comes with a spreadsheet interface, and you can quickly get the hang of its features. 

The fact that you can generate data from ChatGPT without the hassle of manually copying and pasting data from one tool to another makes Gigasheet a seamless solution for backend testing data. 

Give it a try for free and create datasets with millions of rows—no sweat!

The ease of a spreadsheet with the power of a database, at cloud scale.

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