Burst Load platform

Move the complete dataset with parallel work you can operate.

Burst Load profiles the source, divides large tables into bounded slices, moves many independent units concurrently, and records run and failed-task state for review and supported replay.

Plan my migration See high-performance loading

Why this path matters

Turn a database into schedulable work.

Omni Loader inspects tables, keys, sizes, and target behavior before execution. Large tables become concurrent ranges while smaller tables move alongside them, all within explicit worker and bandwidth limits.

  • ✓Automatic large-table slicing
  • ✓Concurrent tables and bounded workers
  • ✓Source, network, and target limits kept visible
  • ✓Project defaults with table-level overrides

What changes for you

Keep reported failures explicit and replayable.

Omni Loader records run, stage, table, and task status. Supported tasks that report failure retain a serialized payload for cleanup and replay; a process or host loss before failure is recorded is not a complete crash-resume checkpoint.

  • ✓Per-table and per-slice progress
  • ✓Replay supported failed work
  • ✓Bandwidth and concurrency controls
  • ✓Console jobs suitable for schedulers and CI/CD

The payoff

See what the capability changes.

Parallel

Tables and slices

Use bounded workers across large tables and independent objects.

Adaptive

Production-minded defaults

Start from measured behavior and tune only the exceptions.

Recoverable

Replay reported failures

Clean up and retry supported tasks that finish with an explicit failure.

Where the work happens

The details that make the result usable.

At a glance

A visual summary of the work.

Illustration of Omni Loader connecting a broad set of data platforms

Specialized outputs

Use the same engine for rows, objects, and analytical loads.

Extract large binary values to object storage, produce compressed warehouse-ready files, or apply precise transformation and mapping rules without replacing the movement engine.

  • Binary and large-object extraction
  • Data-lake and warehouse file generation
  • Project and table customization
  • Saved-project command-line execution

Take the next step

Turn the full move into bounded, inspectable work.

Bring table sizes, keys, source limits, target ingestion, and the migration window. We will map slices, workers, recovery, and delivery.

Plan my migration