Artie
Real-time data replication to your warehouse, now self-serve
Details
- External ID
- 48471805
- Source
- HN
- Company
- —
- Product
- Artie
- Website domain
- artie.com
- Launched
- June 10, 2026
- Cohort
- —
- Upvotes
- 26
- Upvotes percentile
- 0.7759562841530054
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
Hey HN, cofounder of Artie here. We’ve built a real-time data replication tool that captures every row-level change in your source database and streams it to your warehouse in under 60 seconds.The last time I posted here, people had to book a call with us in order to access Artie. Today, that’s no longer the case. You can now connect your source and destination and start streaming immediately.I spent years of my career building large-scale data pipelines and experienced how difficult it was to get real-time data firsthand. I believed there must be a better way to stream data into our warehouse, which resulted in Artie being born. And now with AI agents, reducing data latency has become more and more crucial as agents need to make decisions off of fresh data.When I first started building Artie, I quickly learned that the components meant to keep CDC running smoothly are very much bolted on with tons of edge cases. Unfortunately in practice, they were not built to work together. We ended up dealing with schema drift, backfill race conditions, Kafka offset commits, and TOAST columns. I’d love to know if others have hit these same issues while building in-house.artie.com, would love feedback!
Enrichment
- Theme
- database infrastructure and developer tools
- Vertical
- Horizontal
- Function
- Data infrastructure
- Audience
- B2B
- AI stance
- Not AI
- Project type
- Commercial product
- Normalized one-liner
- real-time data replication to warehouse
- Manually corrected
- False
Could you build this?
No Enterprise real-time CDC replication requires distributed systems expertise for handling database write-ahead logs (PostgreSQL WAL, MySQL binlog), schema evolution, failure recovery, and distributed exactly-once guarantees at scale.
What it would actually take: The architecture requires a distributed streaming engine built in Go or Rust, connecting directly to database replication streams (e.g. pgoutput, binlog) and Kafka or Redpanda, with high-performance consumers batching writes into Snowflake, BigQuery, or ClickHouse. The hard problems include transaction boundary preservation, low-overhead snapshotting under active load, dynamic DDL schema migration handling, and guaranteed exactly-once processing without data loss during crashes.
Discussion
6 comments analyzed.
Competitors mentioned: Debezium
Competitors
Other products that read as similar to this one — 17 launches clear the similarity bar, closest 8 shown.
Attention rank: #5 of 18 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 221 days after the earliest competitor.
- Polynya · hn · 2026-04-26 · 5 upvotes · similarity 0.42
- UnisonDB · hn · 2025-11-01 · 17 upvotes · similarity 0.39
- ObsessionDB · hn · 2026-01-23 · 12 upvotes · similarity 0.37
- StreamHouse · hn · 2026-02-25 · 10 upvotes · similarity 0.36
- Quorum-free replicated state machine built atop S3 · hn · 2026-01-31 · 6 upvotes · similarity 0.36
- DDL to Data · hn · 2026-01-06 · 55 upvotes · similarity 0.34
- FactIQ · hn · 2026-07-08 · 7 upvotes · similarity 0.33
- I built a database for AI agents · hn · 2026-04-07 · 12 upvotes · similarity 0.33
Other launches for this product
Same idea, different domain
Nobody's really built a data infrastructure tool for Media & entertainment yet.