Efficiently transfer tables, indexes, and data from PostgreSQL-based ParadeDB into Amazon Redshift.
Redshift’s columnar storage, elastic scaling, and AWS ecosystem offer faster analytical queries and easier integration with BI tools compared with ParadeDB’s PostgreSQL extension approach.
Most teams choose the "Dump → S3 → COPY" path because it preserves schema, scales on large datasets, and relies only on standard tooling.
Run psql
or parade-cli
to export each table. Parquet is preferable for compressed, typed data.
Use aws s3 cp
or aws s3 sync
. Grant Redshift’s IAM role s3:GetObject
permission on the bucket.
Translate ParadeDB types to Redshift equivalents (e.g., JSONB → SUPER
, TEXT → VARCHAR
). Create DISTKEY
and SORTKEY
for performance.
Run COPY
for each table, pointing to S3. Use FORMAT AS PARQUET
or specify CSV options.
After loading, run count and checksum queries in both systems. Spot-check high-value rows with EXCEPT
queries.
• Migrate small tables first to test.
• Compress files with GZIP or Parquet.
• Use Redshift Spectrum for staging when datasets exceed cluster storage.
• Enable COMPUPDATE OFF
only after initial COPY to avoid sub-optimal encodings.
Yes. Schedule incremental exports using pg_dump --data-only --snapshot
or logical replication to S3, then COPY with TRUNCATECOLUMNS
and TIMEFORMAT 'auto'
.
Cut over after query regression tests pass and CDC pipelines are confirmed. Freeze ParadeDB writes during the final delta load to guarantee consistency.
No. COPY on the parent table reads all shards automatically if you query the parent table.
Yes. Export WAL changes with pglogical, enqueue to S3, and call Redshift’s COPY with MAXERROR 0 on a schedule.
Redshift’s IAM role needs s3:GetObject
on the bucket and kms:Decrypt
if objects are encrypted.