in a Previous article, we explore creating many-to-one relationships between columns in a synthetic PySpark DataFrame. This DataFrame only consisted of foreign key information and we did not generate any textual information that could be useful in a demo DataSet.
Anyone looking to populate an artificial data set will likely want to produce descriptive data, such as product information, location details, customer demographics, etc.
In this post, we will delve into some sources that can be used to create synthetic text data with little effort and cost, and use the techniques to create a DataFrame containing customer details.
Synthetic data sets are a great way to anonymously demonstrate your data product, such as a website or analytics platform. Allow users and stakeholders to interact with example data, exposing meaningful analysis without violating any privacy issues with sensitive data.
It can also be great for exploring machine learning algorithms, allowing data scientists to train models in the case of limited real data.
Performance testing of data engineering pipeline activities is another great use case for synthetic data, giving teams the ability to scale data pushed through an infrastructure and identify weaknesses. in the design, as well as benchmarking execution times.
In my case, I am currently creating an example dataset to test the performance of some Power BI capabilities at large volumes, which I will write about in due time.
The data set will contain sales data, including transaction amounts and other descriptive characteristics such as store location, employee name, and customer email address.
Starting simple, we can use some built-in functionality to generate random text data. Importing the random and chain Python modules, we can use the following simple function to create a random string of the desired length.
