Omni Loader uses a massively parallel, in-memory migration engine. It slices large tables across workers, keeps work flowing, and lets you cap bandwidth when the migration shares infrastructure with production traffic.
TargetBulk ingest Why this path matters
Omni Loader profiles the run, divides large tables into concurrent slices, and keeps multiple tables moving at once. More workers increase concurrency until CPU, source reads, network, or target ingestion becomes the limiting resource.
What changes for you
A full-speed migration can consume the same network and target capacity that applications need. Set a bandwidth ceiling or change worker allocation so the move advances quickly inside an explicit operating budget.
The payoff
A published run demonstrates the upper end of a favorable target path; your source and target set the real boundary.
Workers process independent ranges instead of waiting on one serial reader.
The engine was built for direct movement rather than layered onto a general ETL runtime.
Where the work happens
At a glance
Measure the whole path
Parser, source-to-spool, and target-ingestion figures answer different questions. Size the deployment with a representative source, target, table shape, and network path instead of treating one component number as an end-to-end promise.
Take the next step
Bring a representative source, target, and table shape. We will identify the active bottleneck and show the measured migration boundary on your data.
Plan my migration