Move Redshift tables to ParadeDB fast with SQL-compatible tools and minimal downtime.
Teams switch to ParadeDB for lower costs, open-source control, and PostgreSQL-compatible features that simplify analytics pipelines.
Dump Redshift data to Amazon S3, spin up ParadeDB, then COPY each table from S3 into ParadeDB using parallel workers for speed.
Confirm Redshift UNLOAD permissions to S3, create matching schemas in ParadeDB, and configure the aws_s3 extension or give ParadeDB IAM access.
Run CREATE TABLE … LIKE redshift_schema.table INCLUDING DEFAULTS INCLUDING IDENTITY
to mirror structures, adjusting Redshift-only data types.
Use COPY … FROM PROGRAM 'aws s3 cp s3://bucket/file.gz -'
with FORMAT csv
or PARQUET
for columnar speed. Enable parallel
workers to accelerate large tables.
After the full load, enable wal2json
output plugin in ParadeDB and stream Redshift changes via CREATE PUBLICATION
& logical replication for near-zero downtime cutovers.
Yes. The parade migrate redshift
command orchestrates schema creation, S3 UNLOAD, and ParadeDB COPY automatically.
Validate row counts, sample aggregates, and foreign-key constraints. Update your application connection string to ParadeDB and monitor query latency.
Use identical data types, compress UNLOAD files, load largest tables first, and rehearse in staging. Schedule a brief read-only window for the final delta sync.
The queries below unload two tables to S3, recreate them in ParadeDB, and copy the data.
Yes. ParadeDB is a PostgreSQL fork, so standard SQL, extensions, and drivers work unchanged.
After migration, use logical replication to stream updates both ways, but watch for conflicts on identity columns.
With PARQUET and parallel COPY, expect 200–300 GB/hour on m6i.large nodes. Network bandwidth is usually the bottleneck.