On-Prem Warehouse to Amazon Redshift Blueprint
A blueprint to migrate an on-prem warehouse to Amazon Redshift using AWS SCT, S3 plus parallel COPY loading, distribution/sort-key design, ELT via dbt, and validated cutover with FinOps controls.
Overview
Amazon Redshift is a managed, columnar, massively parallel processing (MPP) data warehouse. It suits teams standardizing on AWS who want a warehouse tightly integrated with S3, Glue, and the broader data stack. Migrating from an on-prem warehouse removes hardware ownership and adds managed scaling, including Redshift Serverless. This blueprint covers the move.
Phases
Assessment. Inventory schemas, views, procedures, and ETL jobs. Use AWS SCT to assess conversion and flag unsupported constructs. Profile data volumes and query patterns.
Schema conversion. Recreate tables and map types. Design distribution styles (KEY, EVEN, ALL) and sort keys to minimize data movement and scan cost, the most important Redshift tuning decision. Or use Redshift's automatic table optimization.
Data load. Export to S3 as Parquet/compressed delimited files and bulk-load with COPY (massively parallel). Use AWS DMS or the SCT data extraction agents for ongoing/CDC loads.
Pipeline migration. Re-point ingestion; favor ELT inside Redshift (dbt). Convert stored procedures to Redshift stored procedures (PL/pgSQL-like) or dbt models. Use Redshift Spectrum to query S3 data in place where it makes sense.
Validation. Reconcile counts and checksums; compare query outputs and BI reports against the source.
Cutover. Switch consumers to Redshift, run in parallel briefly, then decommission the on-prem warehouse.
Key Risks and Mitigations
- Data consistency: Reconcile per-table counts/checksums and validate dashboards before cutover.
- Distribution-key design: Poor distribution causes data skew and slow joins. Analyze join patterns and use automatic optimization or careful key selection.
- Cost overrun: Right-size clusters or use Redshift Serverless; apply pause/resume and concurrency scaling limits (FinOps).
Recommended Tooling
AWS SCT for assessment/conversion; S3 plus COPY for parallel load; AWS DMS for CDC; dbt for ELT; Redshift Advisor and SVL/STL system tables for tuning.
Success Metrics
Lower query latency, higher concurrency (concurrency scaling), and reduced cost versus fixed on-prem hardware.
Prerequisites
An AWS account with IAM, S3 staging, a distribution/sort-key design, FinOps guardrails, and a parallel-run validation plan.