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Real Fake Data
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Real Fake Data

What is Fake Data?

The hows and whys of faking data realistically

Build vs. Buy

De-indentifying your data in-house?
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Customers

Check out who is faking it with Tonic.ai

Real Fake Data
Platform

Features

De-identify, subset, and synthesize your data in an all-in-one platform.

Integrations

Connect natively to the leading SQL and NoSQL databases and data warehouses.

Connectivity

Automate your data pipelines with our API, webhooks, and custom scripts.

Platform
Solutions

By Use Case

Fix your staging environments, ensure compliance, and streamline CI/CD.

By Industry

Real fake data for healthtech, fintech, edtech, and all the techs in between.

Solutions
Pricing
Learn

eBooks

Think an ebook about data synthesis sounds boring? Click here and think again.

Webinars

Register for our Iive(ly) webinars or access recordings.

Blog

The latest insights and news from the Tonic.ai Fakers.

Events

Tonic.ai is coming to a city near your!

Videos

Step-by-step product videos to help you fake it like a pro.

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About
book a demo
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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