Fluxwire reads the write-ahead log the instant a row commits and streams that change — in commit order, exactly once — to every warehouse, cache, search index, and service that depends on it. No batch windows. No drift. No nightly catch-up job that's already three hours behind by breakfast.
● tailing WAL lsn 7F/3A2901C8 lag 84ms slot fluxwire_main
+ orders INSERT id=90412 ✓ delivered off 4471902
~ accounts UPDATE id=1183 ✓ delivered off 4471903
- sessions DELETE id=55027 ✓ delivered off 4471904
! schema change orders.currency added → propagated to sink
throughput 412k rows/s backpressure 0 dupes 0 loss 0The change pipeline behind teams that can't wait for the next batch window
Fluxwire sits beside your database, never in front of it. It taps the replication stream your primary already writes, turns every commit into an ordered event, and keeps every downstream system in lockstep with production — without a single query against your tables.
Fluxwire reads the WAL, binlog, or oplog directly — it never polls and never scans. Your primary doesn't feel a thing, and you capture the events timestamp-and-trigger setups quietly drop: hard deletes, out-of-band updates, and every column of the row as it actually committed.
Every event carries a monotonic offset, checkpoints are durable, and writes to each sink are idempotent on the primary key. A consumer that dies mid-stream resumes on the exact row it left — no gap, no replayed duplicate, no manual reconciliation at the end of the quarter.
Add a column, widen a type, rename a table — Fluxwire reads the DDL inline with the data, evolves the destination schema where the sink allows it, and tags the event so downstream consumers can react. The stream doesn't break at 3 a.m. because someone shipped a migration.
Mask a PII field, reshape a nested payload, drop a column, or filter rows with a small function that runs inside the stream — before the data ever lands. No external job, no second hop through a queue, no copy of sensitive data sitting somewhere it shouldn't.
Snapshot terabytes of history and hand off to the live tail at a consistent watermark — no gap at the seam, no duplicate window to dedupe later. Seed a brand-new warehouse while production keeps taking writes, and the first query is already correct.
Built for the volume production databases actually throw off
Point Fluxwire at the database you already run and the destinations you already use. Declare a pipeline in a few lines of config; the engine owns ordering, retries, backpressure, and recovery so you never hand-write another consumer.
Postgres, MySQL, MongoDB, SQL Server, and DynamoDB — captured straight from the native replication log, with a dedicated slot that never blocks your writes.
Land changes in Snowflake, BigQuery, ClickHouse, Databricks, and Iceberg with type-faithful mapping and merge semantics, not append-only sludge you reconcile later.
Bridge to Kafka, Kinesis, Pub/Sub, and Redpanda when other services need to subscribe to the change — same ordering guarantees, same offsets, no glue code.
Keep Redis, Elasticsearch, and your edge cache hot, invalidated the instant the row of truth moves — so reads stop serving a value that's already wrong.
CDC only earns its keep when it removes work somewhere downstream. Here's what teams wire up in the first week — each one a destination that used to drift between batch runs and now tracks production within a second.
Stream commits into Snowflake or BigQuery so dashboards read this morning's orders, not last night's snapshot. The data team stops fielding 'why is this number stale' before the day even starts.
Reindex Elasticsearch and bust your Redis or edge cache the moment a row changes, so a customer never sees a price, a status, or a balance the database already updated.
Publish ordered change events to Kafka so billing, notifications, and fraud subscribe to the same stream instead of hammering the primary with their own queries every few seconds.
Capture an immutable, ordered log of every insert, update, and delete — who changed what and when — straight from the source, so audit isn't a feature you bolt on after the breach.
Keep a read replica or a disaster-recovery copy in another region continuously in sync, so failover is a routing change, not a frantic restore from last night's dump.
Feed an online feature store from the same change stream, so a model scores on the state of the world right now instead of whatever the last batch job managed to materialize.
“We ripped out a fleet of cron jobs and a homegrown CDC service that broke if you looked at it wrong. Fluxwire does the whole thing in one stream, and our warehouse is finally within a second of prod instead of a day behind.”
“A schema change used to mean a 2 a.m. page and a snapped pipeline. Someone shipped a migration last Tuesday, the DDL just propagated to the warehouse, and I found out from the changelog — not from PagerDuty.”
“We backfilled four terabytes and cut over to live tail with zero gap and nothing to dedupe. Then we triggered two failovers on purpose to try to break the exactly-once guarantee. We couldn't.”
You pay for rows that actually move. Start on one source for free and scale the wire as your write volume does — no per-engineer tax for piping your own data.
For a first pipeline and side projects.
For production data backbones.
For regulated, high-volume estates.
No. Fluxwire consumes the replication log your database already writes — the same stream a read replica reads — through a dedicated replication slot. There are no queries against your tables and no triggers on your writes, so the source carries effectively zero added load. We monitor slot lag and alert before it ever becomes a retention problem.
Every event has a durable, monotonic offset and the stream is checkpointed continuously. A consumer that crashes resumes from its last committed offset, and because every write to a sink is idempotent on the primary key, the replay produces no duplicates downstream. You lose nothing and double-count nothing.
DDL is captured inline with the data, in order. Fluxwire detects the change, evolves the destination schema where the sink supports it — adding columns, widening types — and tags the event so consumers that need to react can. For changes a sink can't apply automatically, it pauses that one stream and tells you exactly why instead of silently corrupting the target.
Yes. Enterprise deploys into your VPC or fully self-hosted, so change data never leaves your network and never touches ours. The cloud and self-hosted engines run the same connectors and the same exactly-once guarantees — the only difference is whose hardware it's on.
Point Fluxwire at the same source, run a consistent snapshot backfill, and hand off to live tail at the watermark with no gap. Most teams run both pipelines in parallel for a day, diff the destinations to confirm parity, then retire the old one. No freeze, no big-bang cutover weekend.
Connect a source, pick a destination, and watch your first change land downstream in under a minute. In order, exactly once, with zero load on your primary. No sales call to start.