Use case
Warehouse data, minutes behind production.
Most CDC pipelines fall behind during peak hours, recover overnight, and run reporting on eight-hour-old data. Wirekite Replicate keeps lag in single-digit minutes, sustained, with the loader applying every change.
Your dashboards are lying.
Snowflake, Google BigQuery, Firebolt, and Databricks all query fast. The problem is what they're querying. If your CDC pipeline is hours behind production, the dashboard on top of it is hours wrong — and the team making decisions from it doesn't know.
The bottleneck is almost never the source. A busy primary can emit hundreds of thousands of changes a second and hand them over without breaking a sweat. The problem is downstream — most CDC tools parse, transform, and apply changes one at a time on the target, and that's where the lag piles up.
Wirekite Replicate captures changes straight from the source and applies them on the target in efficient batches. The loader keeps up with what the source can produce.
On real hardware. On production-shaped workloads.
Sustained — not lab bursts. With the loader applying every change.
Why Wirekite keeps up.
| Capability | Typical CDC tool | Wirekite Replicate |
|---|---|---|
| Source capture | Falls behind a busy primary | Keeps up with the source's full rate |
| Per-change overhead | Heavy — parse and convert every change | Minimal — built for speed |
| Target apply | One row at a time | Efficient batches |
| Type fidelity | Often lossy across dialects | Timestamps, NUMERIC, JSON, NULL mapped exactly |
| Result | Hours behind by peak | Single-digit-minute lag, sustained |
Pick the warehouse. We support it.
Wirekite Replicate writes into every major analytical target.
Need your warehouse current?
Book a 30-minute demo. We'll set up a CDC pipeline against your workload, live, end-to-end.