Cloud and warehouse · Staging

Prepare data in parallel. Let the destination ingest natively.

Omni Loader can transform rows into intermediate files, stream them to object storage, and invoke target-side ingestion or stop after file creation when the files are the deliverable.

Plan my migration Explore warehouses

Why this path matters

Stage where the target can ingest efficiently.

Write to Amazon S3, Azure Blob Storage, Azure Data Lake Storage Gen2, Fabric Lakehouse, Google Cloud Storage, or local storage.

  • ✓CSV, Parquet, and Avro
  • ✓Gzip and Snappy options
  • ✓Row-count file splitting
  • ✓Configurable path layouts

What changes for you

Decide what happens before and after the load.

Use timestamped output, clear-before-load, folder or table cleanup, overwrite behavior, and clear-after-load according to the migration workflow.

The payoff

See what the capability changes.

3 formats

CSV · Parquet · Avro

Choose the intermediate shape the target expects.

6 locations

Cloud or local staging

Use S3, Azure, Fabric, GCS, or local storage.

Bounded

Streaming backpressure

Keep producers from outrunning uploads indefinitely.

Where the work happens

The details that make the result usable.

At a glance

A visual summary of the work.

Illustration of source data moving into an analytical destination

Intermediate remapping

Separate file schema from final warehouse schema.

Warehouse targets can ingest an intermediate representation and complete conversion inside the target, avoiding slow row-by-row loading.

Take the next step

Feed the warehouse the way it wants to ingest.

Tell us the destination, staging location, format, compression, and cleanup policy.

Plan my migration