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.
Burst Load · Performance
Move the full dataset at the speed of the available system.
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.
Burst LoadRun multiple tables in parallel. Slice the largest ones further.
Omni Loader
orders2.4B rows
S01–S04 events680M rows
S05–S07 customers92M rows
S08–S09 Omni Loader Slice · schedule · load
TargetBulk ingest The job to be done
Move the full dataset without turning failure into a restart.
A large migration is not finished when the first rows arrive. Omni Loader keeps parallel work productive, adapts the loading path to the target, and records enough progress to recover the part that failed.
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.
Why this path matters
Use every useful core without hand-partitioning the job.
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.
The outcome
Bring a representative source, target, and table shape. We will identify the active bottleneck and show the measured migration boundary on your data.
The payoff
See what the capability changes.
10.5M rows/sec
Measured BigQuery run
A published run demonstrates the upper end of a favorable target path; your source and target set the real boundary.
N-way
Parallel table slices
Workers process independent ranges instead of waiting on one serial reader.
In memory
No framework spill path
The engine was built for direct movement rather than layered onto a general ETL runtime.
Where the work happens
The details that make the result usable.
Read each capability as part of the operating path. The important question is not whether a feature exists, but what work it removes and what evidence it leaves behind.
At a glance
A visual summary of the work.
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.
Parallel in-memory engine
Use every useful core without hand-partitioning the job.
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.
- Automatic slicing for very large tables
- One worker per core as a practical starting point
- Configurable local worker concurrency
- Progress visible per table and slice
Production coexistence
Fast does not have to mean uncontrolled.
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.
Measure the whole path
Benchmark boundaries stay visible.
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.
Before you choose
Check the path against your environment.
Use the page-specific details below as a short discovery checklist. They are the conditions and work areas that shape the capability, not generic product promises.
- Automatic slicing for very large tables
- One worker per core as a practical starting point
- Configurable local worker concurrency
- Progress visible per table and slice
The path to confidence
See the work. Make the call. Prove the result.
The page keeps the product detail close to the decision it supports. Your team can see what the software handles automatically, what it changes for the target, and what the result leaves ready to operate.
Use every useful core without hand-partitioning the job.
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.
Fast does not have to mean uncontrolled.
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.
Benchmark boundaries stay visible.
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
Test Burst Load on the path you actually need.
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