The article explains methods for generating synthetic descriptive data in PySpark. It covers various sources for creating textual data, including random characters, APIs, third-party packages like Faker, and using Large Language Models (LLMs) such as ChatGPT. The techniques mentioned can be valuable for populating demo datasets, performance testing data engineering pipelines, and exploring machine learning algorithms. The end of the article discusses implementing the synthetic data into a DataFrame using PySpark functionalities and concludes by inviting readers to share their own methods for generating synthetic datasets.
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Generating Synthetic Descriptive Data in PySpark
Use various data source types to quickly generate text data for artificial datasets.
Why create a synthetic dataset?
Synthetic datasets are a great way to anonymously demonstrate your data product, such as a website or analytics platform. It can also be great for exploring Machine Learning algorithms and performance testing Data Engineering pipeline activities.
Random characters
Starting off simple, you can use some built-in functionality to generate random text data. This kind of data generation is very generic, with limited applications in demo datasets.
Benefits and Limitations
This kind of data generation is very generic, with limited applications in demo datasets. It can be combined with other string generation techniques to give a bit more value at very little effort.
APIs
APIs are a great source of information and can provide representative data for various topics such as currency rates.
Benefits and Limitations
Getting data from APIs can vary in complexity and security requirements can be off-putting. Third-party packages like Faker can be very high-impact and low-cost in both price and time.
ChatGPT
Large Language Models (LLMs) like ChatGPT can be a great asset in data generation, showing strengths in rapid data generation. However, reliability and consistency of LLMs is being heavily investigated at the same time.
Putting this into a DataFrame
There are a few choices of how to implement the synthetic data at this stage. UDFs and Databricks Labs Data Generator are some options to consider.
Conclusion
We’ve outlined a variety of methods to generate textual synthetic data quickly, allowing us to accelerate our demonstrative dataset creation. All the examples above can be extended, refined, and tailored to your specific use-case.
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