Change happens.
We deliver.
Every committed change in your database, delivered in order and
at-least-once, anywhere it needs to go: analytics, search,
services, your data lake, etc. From a single binary,
no JVM, no Kafka Connect, no cluster to babysit.
Moving data shouldn’t cost you a team.
Today the options are heavy, expensive, or fragile. Here’s what data teams are actually living with.
Nightly ETL means decisions get made on yesterday’s numbers — and fraud is caught 12–24 hours too late.
Debezium means a JVM, heap tuning, and offsets that drift. Every deployment mode requires JVM operations. A pipeline that needs a pipeline team.
SaaS tools charge per row synced. Your costs scale with your success — a misaligned incentive by design.
pg_logical is Postgres-to-Postgres only — no fan-out, no transforms. Roll your own with polling or webhooks and changes land out of order, or not at all.
Nightly backups can’t rewind a bad migration. Without point-in-time recovery and REDO, you can’t replay to the exact moment before disaster — you lose everything since the last snapshot.
Re-index crons leave Elasticsearch hours stale — users search for items that exist but can’t be found.
Six capabilities. Two modes.
The industry sells these as six separate products. They’re not. They all start by reading the transaction log — five decode it into row-level events, one archives the raw bytes for full-cluster recovery.
Parse the log into ordered, row-level events. CDC, replication, heterogeneous sync, audit, and logical PITR are all just sinks on this one stream.
Stream or archive the raw log bytes for full-cluster recovery. File-based for higher RPO, streaming for seconds. No broker in the path.
One pipeline, a dozen jobs it retires.
Kill the nightly ETL. Dashboards reflect reality with sub-second lag.
New rows searchable in milliseconds. No re-index cron, ever again.
One source, many sinks, in order. Replace N polling integrations.
WAL-level capture. 100% complete, cannot be bypassed by application code.
Continuous JSONL to S3. Decommission the Airflow DAGs.
Embeddings stay current with the source. No stale RAG context.
Stream one engine into another — Postgres→MySQL, Oracle→Kafka — table by table, transformed in-flight.
Archive decoded events and replay any table to any moment — logical PITR without a backup window.
Re-run a stream from any offset to rebuild a sink that fell behind — no re-snapshot of the source.
“Every line of production code exists to move data from point A to point B — without losing it, duplicating it, or reordering it.”
“Trust comes from reliability. Reliability comes from testing. A rigorous test suite is not overhead — it’s the product.”
“We handle other people’s data. A pipeline that silently drops events is worse than having no pipeline at all — because the operator believes replication is happening when it isn’t.”
“Six capabilities. Two underlying modes. One binary. We didn’t build six products — we learned to read the transaction log correctly.”
“The question isn’t whether your pipeline works when everything is fine. It’s what happens when things go wrong — and ‘things go wrong’ has far more surface area.”
“Every line of production code exists to move data from point A to point B — without losing it, duplicating it, or reordering it.”
“Trust comes from reliability. Reliability comes from testing. A rigorous test suite is not overhead — it’s the product.”
“We handle other people’s data. A pipeline that silently drops events is worse than having no pipeline at all — because the operator believes replication is happening when it isn’t.”
“Six capabilities. Two underlying modes. One binary. We didn’t build six products — we learned to read the transaction log correctly.”
“The question isn’t whether your pipeline works when everything is fine. It’s what happens when things go wrong — and ‘things go wrong’ has far more surface area.”
Keep your vector DB in sync in real time.
The AI Pack adds an embedding transformer and vector-DB sinks — no cron, no drift, no stale RAG context.
Deploy once. Forget the pipeline exists.
Your data arrives where it needs to be, in order, with delivery guarantees — and your team never thinks about it again.