Data Warehouse Migration
Move data between warehouses with automatic schema discovery, incremental syncs, and checkpoint-based resume. Migrate without building throwaway scripts.
The challenge
Migrations require custom scripts
Moving from Redshift to BigQuery or PostgreSQL to Snowflake typically means writing one-off migration scripts that are discarded after use.
Schema differences cause failures
Data types, constraints, and naming conventions differ between databases. Manual type mapping is tedious and error-prone at scale.
Large tables are hard to move reliably
A network interruption or timeout halfway through a billion-row table means starting over from scratch without checkpoint support.
How StreamFlows solves it
Automatic schema discovery
StreamFlows reads the source schema and creates matching tables in the destination with proper type mappings. No manual DDL required.
Checkpoint and resume
If a migration is interrupted, it resumes from the last successful batch. No data loss, no re-processing of rows already written.
Incremental catch-up
After the initial migration, keep the destination in sync with incremental syncs. Run both warehouses in parallel during cutover.
Cross-database type mapping
Redshift, PostgreSQL, MySQL, BigQuery, and Snowflake types are mapped automatically. Override mappings per table when needed.
Relevant connectors
StreamFlows connects to the tools your team already uses.
How your data flows
From your sources through StreamFlows into your destination warehouse.
Ready to consolidate your data?
Set up your first pipeline in minutes. Connect a source, pick your streams, and start syncing to your warehouse.