Single service comparison

Huawei Cloud DWS is the direct equivalent: a managed analytical warehouse built on a PostgreSQL-derived engine, deployed as elastic clusters (up to 2048 nodes, ~10 PB) rather than Redshift's per-workgroup serverless endpoint. Both use PostgreSQL SQL dialects, but Redshift's APIs (JDBC/ODBC, data API, COPY from S3, Spectrum, Redshift Serving), system views, and workload-management (WLM queues, concurrency scaling) differ from DWS's logical-cluster, resource-pool, and Snapshot/DR model. Redshift's federation targets RDS/Aurora via cross-database; DWS federates to OBS-based lakes and logical clusters within one cluster. Treat SQL object semantics, drivers, and admin tooling as non-identical and re-qualify each.

Scaling and HA boundaries diverge. Redshift separates compute from storage in ra3 (managed storage backed by S3) and supports data sharing across clusters; DWS uses shared-nothing GaussDB-style nodes with elastic scaling, multi-layer HA, and OBS read/write for lakehouse (HTAP) workloads. DWS exposes logical clusters and resource pools for workload isolation instead of Redshift's WLM queues plus concurrency scaling; replication, snapshot, and cross-region DR procedures are Huawei-specific. Integration surfaces also differ: Redshift integrates with AWS Glue, Lambda, QuickSight and Lake Formation; DWS pairs with CDM, DLI, MRS, DIS, and DataArts Studio for ingestion, transformation, and governance.

Operational responsibility and ecosystem tooling differ. Redshift is controlled via AWS console/CLI/IaC, with Redshift Serverless, ML-driven column encoding, materialized views, and automatic table optimization; DWS is operated through Huawei Cloud console, GaussDB(DWS) tooling, snapshot/backup, and monitoring. Syntax compatibility tooling is asymmetric: DWS ships migration aids for Teradata/Oracle/Greenplum and a DSC syntax-rewrite tool, but no first-party Redshift-to-DWS path is documented, so SQL refactor (distribution/sort keys, COPY, UDFs, stored procedures) must be mapped manually by a migration architect.

Assess scope and pick the target shape before moving anything. Map Redshift schemas, distribution/sort keys, WLM queues, UDFs, and external/Spectrum workloads to DWS equivalents; for pure federated data-lake query on S3/OBS, evaluate whether DLI (serverless SparkSQL/SQL on OBS) better fits ad-hoc lake analytics, and use MRS only for Spark/Hive pipeline workloads. Do not assumes Redshift feature parity exists; create a feature matrix (control plane, SQL, concurrency, snapshot/DR, billing) and validate per Huawei region before committing to a cutover.

For data movement, use Cloud Data Migration (CDM) as the batch mover between Redshift (via JDBC) and DWS, placing CDM and the DWS cluster in the same region/VPC for throughput and lower egress. Determine network connectivity (private/Direct Connect or public IP) early; CDM handles full table loads and incremental jobs, but Redshift-unique loading (COPY from S3, Spectrum) must be retargeted to OBS-backed loads or DIS streaming into DWS. Encrypt credentials and route through Huawei KMS/SSL; confirm CDM supports the source connector and column types at scale before relying on it for production.

Refactor application SQL and schema. Both engines are PostgreSQL-derived, but distribution styles (KEY/EVEN/ALL vs. Redshift KEY/ALL/EVEN), sort keys, compression encodings, COPY commands, leader-node-only functions, and stored procedures differ. Use the Huawei DSC syntax-migration tool where it can re-target SQL, but expect manual rewrite of Redshift-specific system tables (STL/SVL), data-api calls, and Python/SQL UDFs. Recreate distribution keys to preserve collocated joins, and re-benchmark representative queries on a sized DWS cluster.

Validate then cut over, and re-baseline cost. Run parallel execution: replicate data via CDM, replay a representative workload, and compare row counts, query plans, and SLAs; promote via DNS/endpoint switch and snapshot-based rollback. Re-model TCO: Redshift bills node-hour or serverless RPU-second plus managed-storage/scan (Spectrum), while DWS bills cluster-spec (vCPU/memory/storage) hour plus storage and OBS/lake traffic; account for cross-region and interconnect traffic, retention period, and peak load rather than assuming parity.

Mind the gaps. No documented one-click Redshift-to-DWS migration exists; federated query, Redshift ML, data sharing, and Redshift Serverless autoscale have no exact DWS analog and may require DLI/MRS composition or application redesign. Regional availability, quotas (max node count, per-cluster storage), and IAM/KMS mapping differ by Huawei region, so confirm limits and identity integration before production cutover.