Redshift Cloud Native lets you spin up fully-managed, auto-scaling Amazon Redshift clusters and run PostgreSQL-compatible SQL without capacity planning.
Cloud-native Redshift is serverless. It auto-scales compute and storage, charges per second, and keeps PostgreSQL syntax, so you write the same SQL but forget about nodes and vacuum jobs.
In the AWS console pick “Redshift Serverless,” name a workgroup, pick an IAM role, then connect with any PostgreSQL client using the generated host, port 5439, and database name.
Use CREATE TABLE
with DISTKEY
and SORTKEY
to tell Redshift how to distribute and order data for fast scans.
Run COPY
from an S3 URI. Redshift pulls files in parallel, so load terabytes in minutes. Make sure the IAM role has s3:GetObject
permission.
Choose a frequently filtered column (e.g., customer_id
on Orders
) as DISTKEY
; pick a time column (e.g., order_date
) as SORTKEY
. Queries on recent orders then touch a single slice and scan only fresh blocks.
Create separate schemas for raw, staged, and analytics data to keep pipelines tidy. Use fully-qualified names (analytics.orders
) to avoid naming collisions.
Use the Redshift console “Monitoring” tab to watch concurrency, query duration, and slot usage. Pause your serverless workgroup when idle to stop the meter.
Split source data into 100–250 MB gzip files so each slice reads a file in parallel. Avoid a single multi-gigabyte object; it becomes a bottleneck.
Enable AUTOMATIC TABLE OPTIMIZATION
. Redshift analyzes workload patterns and rewrites DIST
/SORT
keys for you.
Yes. Supply the JDBC/ODBC endpoint or host, port 5439, database name, user, and password exactly as you would with PostgreSQL.
Only a subset. Functions like json_extract_path_text
work, but C-based extensions such as PostGIS are not supported.
Serverless workgroups auto-pause after a configurable idle period (default 30 minutes). You can also call update-workgroup
to disable capacity reservation immediately.