Amazon Redshift Data Warehouse Modernization Playbook (2026): Zero-ETL, Serverless, and Spectrum

Zero-ETL integrations make operational data available in Amazon Redshift without you maintaining a classic ETL fleet for supported sources — including Aurora MySQL/PostgreSQL, RDS MySQL/PostgreSQL/Oracle, DynamoDB, and listed SaaS/apps (Redshift zero-ETL). In March 2026, Redshift announced concurrency scaling support for auto-copy and zero-ETL, so ingest peaks can add compute instead of stalling dashboards (What’s New).

This is the warehouse modernization playbook. It is not Serverless vs provisioned tier choice, not the S3 Tables / Iceberg lake reference architecture, and not a Glue-only ETL tutorial.

Artifacts: path matrix, TCO worksheet, architecture diagram (draw.io).

Benchmark silhouette (not a cited client) — Retail analytics, legacy DW ~42 TB logical, 18 nightly Glue jobs, dashboard freshness ~6 hours. After Aurora + RDS zero-ETL into Redshift Serverless and Spectrum for cold history: 11 Glue jobs retired, freshness ~15 minutes on operational facts. Monday open spikes absorbed via concurrency scaling on zero-ETL ingest (post–March 2026). Modeled monthly TCO drop ~$30k → ~$24k before legacy license exit (worksheet).

Modernization sequence

1. Assess — freshness SLOs, license cost, job inventory
2. Land — zero-ETL for supported sources; Glue only for true transforms
3. Size — Serverless default; RA3 if flat-high baseline (tier post)
4. Park cold — S3 / Iceberg + Spectrum; lake detail in modern data lake post
5. Cut over — BI first, then delete duplicate jobs

Opinionated take: Delete Glue jobs only after consumers read the zero-ETL schema for two billing cycles. Early deletion is how you recreate the pipeline under an incident bridge.

Reference flow

Aurora / RDS / DynamoDB ──► Zero-ETL ──► Redshift (Serverless or RA3)
On-prem / complex transforms ──► Glue ──► Redshift + S3
S3 cold / Iceberg ──► Spectrum / lake query ──► same BI tools

Target prerequisites include case sensitivity and IAM/resource policy setup (configure Redshift target).

Path matrix

Use modernization-path-matrix.md to choose Serverless vs RA3 vs zero-ETL vs Spectrum-heavy designs.

What broke — case sensitivity

What broke — Day 2 of zero-ETL. Integration stuck / tables mismatched because enable_case_sensitive_identifier was off on the Serverless workgroup. Detection: integration status + missing relations in the destination database. Fix: enable case sensitivity, recreate destination DB from integration, replay validation queries. Four hours lost — documented as a hard prerequisite in the runbook.

What to Do This Week

1. List every nightly job and tag replicate vs transform.
2. Enable zero-ETL on one Aurora or RDS source into a Serverless workgroup.
3. Turn on case sensitivity and confirm concurrency scaling eligibility in your Region.
4. Fill the TCO worksheet with your license line items.

What This Post Doesn’t Cover

• Detailed Serverless RPU tuning (see tier-choice post)
• Full lakehouse governance (SageMaker Catalog operating model)
• Streaming-first architectures (Kinesis/MSK)
• Oracle Exadata-specific exit tooling beyond DMA/partner programs