Gretel Blog

Create a fair, balanced, privacy preserving version of the 1994 US Census dataset using gretel-synthetics.

In this post, we walk through building a data pipeline that will automatically transform datasets so they can be safely used in development environments.

We are pleased to share that Gretel raised $12M in Series A funding. We're picking up strong momentum in our mission to help developers create safe data.

Create a simple workflow to perform Named Entity Recognition (NER) on sample data using Gretel and load the records into Elasticsearch.

We are releasing new features that make working with data easier by helping you deep dive into records, use blueprints to auto-anonymize data, and more.

We are launching Gretel Blueprints, making it easy to anonymize and balance datasets with just a few clicks.

Gretel's Premium SDK now includes detailed reporting that shows you how accurate your synthetic data's statistical distributions and correlations are.

Create differentially private, synthetic versions of datasets and meet compliance requirements to keep sensitive data within your approved environment.

Learn how we are improving our product by adding new features that make connecting to Gretel easier, faster and more streamlined.

We recently launched our new entity stream view in Gretel Cloud. See how you can view record streams from tagged entities in your data projects.

Learn more about how Gretel's REST APIs automatically build a metastore that makes it easy to understand what is inside of your data.

We founded Gretel based on our beliefs that data shouldn’t be scary.