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nucleuscloud
neosync
Go2023–2026
nucleuscloud / neosync · Archived

neosync

Open Source Data Security Platform for Developers to Monitor and Detect PII, Anonymize Production Data and Sync it across environments.

3Distinct people
15Mentions
0Explicit recommenders
4.1KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Open Source Data Security Platform for Developers to Monitor and Detect PII, Anonymize Production Data and Sync it across environments.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 3:30:57 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month15 dated mentions

Observed mentions: 2023-12-07 18:39:25 UTC to 2026-03-05 14:04:41 UTC.

01232023-12-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 commentsDec 20232024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-02-01 00:00:00 UTC to 2024-03-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-09-01 00:00:00 UTC to 2024-10-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2024-10-01 00:00:00 UTC to 2024-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 commentsJan 20252025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMar 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Dec 20232023-12-01 00:00:00 UTC2024-01-01 00:00:00 UTC220
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC101
Feb 20242024-02-01 00:00:00 UTC2024-03-01 00:00:00 UTC101
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC000
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC101
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC211
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC101
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC000
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC000
Sep 20242024-09-01 00:00:00 UTC2024-10-01 00:00:00 UTC110
Oct 20242024-10-01 00:00:00 UTC2024-11-01 00:00:00 UTC101
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC000
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC303
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC000
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC000
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC000
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC000
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC000
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC110
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC000
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC000
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC000
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC101

What Hacker News says

2 sources
we're actually working on this right, can see the PR here -> https://github.com/nucleuscloud/neosync/pull/1832/files it's a combination of creating a random number of records for foreign keys i.e 1 customer - create between 2 and 5 transctions. Working on giving you control over that, and handling referential integrity with table constraints (foreign keys, unique constraints, etc.) ML based approaches typically are not very good at this and struggle with handling things like referential integrity. So a more "procedural" or imperative way is slightly better. The ideal is a combination of both.
In Show HN: Neosync – Open-Source Data Anonymization for Postgres and MySQL
edrenova·May 22, 2024Submission

Show HN: Neosync – Open-Source Data Anonymization for Postgres and MySQL

Hey HN, we're Evis and Nick and we're excited to be launching Neosync (https://www.github.com/nucleuscloud/neosync). Neosync is an open source platform that helps developers anonymize production data, generate synthetic data and sync it across their environments for better testing, debugging and developer experience. 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 produ
Read the rest
ction 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!
246 points · Open discussion

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