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Please Welcome… Tonic.ai’s Big Book of Data Generators!

Author
Abigail Sims
August 29, 2022
Please Welcome… Tonic.ai’s Big Book of Data Generators!
In this article

    Hey everyone! The Fakers over here in Tonic-land are so excited to introduce our latest and greatest masterpiece… The Great Big Book of Data Generators! In this stunning and super-colorful addition to our ebook library, we cover everything a data mixologist needs to know about using our generators for data mocking small, large, and everything in between. 

    Don’t have time to dive in right now? No sweat. Skim the below to hit the highlights + get a real drink recommendation for later. 

    Okay, I Gotta Ask… What’s a Data Generator?

    It’s really quite simple. (Okay, maybe not that simple.) A data generator is a term used to refer to a certain type of software or even a platform that creates mock or “fake” data. Often, but not always, that fake data can be based on real data. 

    A data generator is any algorithm, script, tool, or system designed to generate synthetic data. 

    Here at Tonic.ai, we use advanced technology to create mock or synthetic data based on real-world sources.

    Covering Common Data Generation Concepts

    Under the cocktail umbrella of data generation at Tonic, there’s a truly formidable number of ways to slice and dice data generation approaches. And in order to understand our 50+ generators, there’s a couple key concepts it’s important to understand. These concepts include…

    • Consistency: Map an input to the same mimicked output across your entire range of tables—or even databases.
    • Linking: Link the generators you apply to certain columns to work together as one.
    • Deep Neural Networks: Use neural networks to ensure high-fidelity pattern-seeking and heavy number crunching.
    • Primary Key Generators: Tonic uses format-preserving encryption (FPE) to apply generators to primary key columns while maintaining data integrity.
    • Differential Privacy: Differentially private data cannot be reverse engineered, re-identified, or otherwise compromised, meeting the stringent guidelines of GDPR and similar privacy laws.

    These are some of the core concepts you’ll see reflected throughout Tonic’s generators–so make sure to read up before moving on to the complete glossary. 

    The Periodic Table of Tonic’s Generators

    Now we’re getting down and dirty. The Periodic table is essentially a quick-glance menu of Tonic’s options. Our 50+ unique data generators can be categorized into five types based on the technology and algorithms at work, as well as the purpose need the generator for in the first place.

    We look at each category of generator—from statistical to ML and beyond—and explore what they are, how they work, and common use cases. 

    From Randomized to ML, We Do it All

    In the second half of The Great Big Book of Generateors, we get down to business. In this section, we go through each category of generator in order, and look at each individual generator. Yeah, you heard us. Every. Single. One. 

    The list of generator categories goes like this…

    • Randomized generators
    • Scramble (x) generators 
    • Search and replace generators 
    • Statistical generators 
    • Machine learning generators 
    • Modifiers

    If you have questions about what generator to use to achieve a specific outcome, or which category to explore in a particular use case, we’ve got you covered. 

    Need more to know more? Lucky for you, there’s more where all this came from. To learn more about Tonic’s data generators, be sure to check out our recipe collection for data mixologists. Or, if you’re feeling super dedicated, join our Mixology 101 webinar to chat with the Fakers who designed the generators themselves. 

    Either way, pour yourself a cold one and enjoy!🍹

    Download the Great Big Book of Data Generators here.

    Abigail Sims
    Marketing
    As a reformed writer now deep in the marketing machine, Abigail can (and will) create narrative-driven content for any technical vertical. With five years of experience telling brand stories for tech startups and small businesses, she thrives at the intersection of complex data and creative communication.
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