Most developers and teams have some version of a database seed script that creates some mock data for their local and stage databases. The problem is that production data is messy and it’s very difficult to replicate that with mock data. This causes two big problems for developers.

The first problem is that features seem to work locally/stage but have bugs and edge cases in production because the seed data you used to develop against was not representative of production data.

The second problem we saw was that debugging production errors would take a long time and would often resurface. When we see a bug in production, the first thing we want to do is reproduce it locally, but if we can’t reproduce the state of the data locally, then we’re kind of flying blind.

Working directly with production data would solve both of these problems but most teams can’t because of: (1) privacy/security issues and (2) scale. So we set out to solve these two problems with Neosync.

We solve the privacy and security problem using anonymization and synthetic data. We have 40+ pre-built transformers (or you can write your own in code) that can anonymize PII or sensitive data so that it’s safe to use locally. Additionally, you can generate synthetic data from scratch that fits your existing schema across your database.

The second problem is scale. Some production databases are too big to fit locally or just have more data than you need. Also, in some cases, you may want to debug a certain customer’s data and you only want their data. We solve this with subsetting. You can pass in a SQL query to filter your table(s) and Neosync will handle all of the heavy lifting including referential integrity.

At the core of Neosync does three things: (1) It streams data from a source to one or multiple destination databases. We never store your sensitive data. (2) While that data is being streamed, we transform it. You define which schemas and tables you want to sync and at the column level, select a transformer that defines how you want to anonymize the data or generate synthetic data. (3) We subset your data based on your filters.

We do all of this while handling referential integrity. Whether you have primary keys, foreign keys, unique constraints, circular dependencies (within a table and across tables), sequences and more, Neosync preserves those references.

We also ship with APIs, a Terraform provider, a CLI and Github action that you can use to hydrate a CI database.

Neosync is an open source project written in Go and Typescript and can be run on Docker Compose, Bare Metal, or Kubernetes via Helm. You can also use our hosted platform or managed platform that you can deploy in your VPC. We also have a hosted platform with a generous free tier - https://neosync.dev

Here's a brief loom demo: https://www.loom.com/share/ac21378d01cd4d848cf723e4960e8338?...

We'd love any feedback you have!