Open Data Infrastructure
How to Migrate from Amazon Redshift to an Open Lakehouse
A phased migration plan that preserves data contracts, governance, and query continuity while moving to open formats.
A Redshift migration fails when it is treated as “move the data.” The real migration is the contract layer: schemas, table semantics, permissions, and the operational routines people rely on.
Why teams move
Teams usually move for one of three reasons: cost predictability, workload flexibility, or platform control. Redshift can be the right choice for many workloads, but migrations happen when requirements shift. AI-assisted analytics, multi-engine needs, open governance requirements, and cross-cloud constraints are common triggers.
Phase 0: inventory and contract boundaries
Before you move anything, inventory what actually exists.
- Tables and consumers: which datasets are critical, and who depends on them.
- Semantics: keys, deduplication rules, late-arriving data behavior, and time-travel expectations.
- Security and governance: permissions, masking, and audit expectations.
- Workloads: BI, ad hoc analytics, batch pipelines, operational queries, and ML feature work.
Pick a pilot domain where the value is clear and the blast radius is manageable. You need operational muscle, not a heroic big-bang plan.
Phase 1: export and land data in open storage
The fastest path to open storage is usually exporting to object storage in an open file format.
- Use Redshift export capabilities to unload tables to object storage.
- Prefer columnar formats for analytics use cases.
- Write exports in a partitioned layout that matches the likely query patterns.
- Keep exports reproducible, with manifests and validation logs.
Do not treat this as “dump files to a bucket.” Treat it as the beginning of a governed storage contract.
Phase 2: build open tables and validate
Once data is in object storage, create an open table format contract (for example, Iceberg) and load or register the data into that contract.
Validation should be boring and repeatable:
- Row counts and checksum sampling: verify content parity for critical tables.
- Query parity tests: run a representative set of BI and transformation queries.
- Semantic edge cases: null handling, timestamp behavior, and dedup rules.
- Governance coverage: validate permissions, auditability, and lineage capture.
Core idea: treat “trust parity” as a first-class deliverable, not a side effect.
Phase 3: cutover without drama
The safest cutover is incremental and reversible.
- Parallel run: keep Redshift and the lakehouse in sync for a period while consumers validate.
- Dual-write where needed: for critical domains, write to both until confidence is earned.
- Gradual consumer migration: move dashboards, jobs, and services in a planned sequence.
- Decommission intentionally: do not shut down Redshift until governance and audit expectations are satisfied in the new path.
If the team cannot reverse the cutover, it is not a cutover plan. It is a bet.
Sources to start with
Start with Redshift export documentation and the table format specification you plan to adopt.