September 13, 2021

Fake Data Anti-patterns

Learn what to avoid and what’s required in order to create realistic, useful test data that looks, acts, and feels just like production.

We’ve identified the 9 most common ways your synthetic data can fail you — and the solutions you need to ensure safety and utility in your test data.

There are wrong ways to fake your data. These data generation pitfalls can break your testing or, worse, leak sensitive data into unsecured environments.

Gain insight into generating realism in:

time series data and event pipelines,
categorical data distributions,
consistency in JSON blobs,
outliers at risk of re-identification,
and working across SQL and NoSQL databases.

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Don't fail at faking it. We're here to help.