This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
rsync.ai
Describe a data pipeline in plain English
rsync.ai moves data between databases, warehouses and APIs (batch or CDC), models it with scheduled SQL, and turns it into dashboards and Slack alerts. Free to try at app.rsync.ai. The pipeline and SQL-model core is source-available to self-host.
After running data infrastructure for years, we built rsync.ai to fix the biggest pain points with modern ETL/ELT tools: unexpected schema breaks, black-box pipeline execution, and having to bolt together dbt, a BI tool and an alerting tool just to get an answer from your data.
It moves data between databases, warehouses, and APIs. Run it as a batch, as CDC (snapshot + changes), or as CDC changes-only. Describe a pipeline in plain English and approve its plan before anything runs.
💻 The pipeline and SQL-model core is source-available at https://github.com/rsync-ai/rsync.ai for self-hosting. Dashboards, workflows and Ask rsync.ai run on Cloud. If you need the full platform on your own network, tell me.
Two questions: what's the data job you most want to stop doing by hand? And which source should we support next?
About rsync.ai on Product Hunt
“Describe a data pipeline in plain English”
rsync.ai was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #79 on the daily leaderboard. rsync.ai moves data between databases, warehouses and APIs (batch or CDC), models it with scheduled SQL, and turns it into dashboards and Slack alerts. Free to try at app.rsync.ai. The pipeline and SQL-model core is source-available to self-host.
On the analytics side, rsync.ai competes within Developer Tools, Artificial Intelligence and Data & Analytics — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how rsync.ai performed against the three products that launched closest to it on the same day.
Who hunted rsync.ai?
rsync.ai was hunted by Rahul Vishnoi. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of rsync.ai including community comment highlights and product details, visit the product overview.
Rahul Vishnoi
Hey Product Hunt! 👋
I'm Rahul, founder of rsync.ai.
After running data infrastructure for years, we built rsync.ai to fix the biggest pain points with modern ETL/ELT tools: unexpected schema breaks, black-box pipeline execution, and having to bolt together dbt, a BI tool and an alerting tool just to get an answer from your data.
It moves data between databases, warehouses, and APIs. Run it as a batch, as CDC (snapshot + changes), or as CDC changes-only. Describe a pipeline in plain English and approve its plan before anything runs.
From there, you can:
• Model it with your own scheduled SQL
• Build dashboards on the same data
• Build visual workflows (query → check → Slack alert)
And if a source you need isn't built in, you can generate a REST or GraphQL connector from the API docs.
☁️ Free to try at app.rsync.ai.
💻 The pipeline and SQL-model core is source-available at https://github.com/rsync-ai/rsync.ai for self-hosting. Dashboards, workflows and Ask rsync.ai run on Cloud. If you need the full platform on your own network, tell me.
Two questions: what's the data job you most want to stop doing by hand? And which source should we support next?