Single service comparison

DWS is the closest Huawei equivalent and is a GaussDB-based, PostgreSQL-derived MPP warehouse offered as a managed cluster (node-flavor based) rather than a DWU-scaled pool. DWS exposes a PostgreSQL-compatible SQL dialect with Oracle/Teradata compatibility layers, not the T-SQL surface Azure Synapse Dedicated SQL Pool uses, so statements, stored procedures, system views, and metadata catalogs must be translated rather than lifted verbatim. Synapse binds the pool to a workspace and separates compute pause from storage by default, whereas DWS typically runs as a persistent cluster with snapshot and elastic-network-interface-style scaling.

Scaling and HA differ materially. Synapse Dedicated SQL Pool lets you pause compute while keeping columnar storage billed, and scales by DWU/CU sizing on a single pool instance. DWS scales by adding nodes to a cluster (certified up to 2048 nodes) and uses logical clusters, snapshot backups, and node-level HA rather than a pause/resume model, meaning idle-cost optimization patterns do not map directly. Architects must re-plan for always-on cost behavior and explicit resource pools/Workload Management on DWS instead of Synapse workload classification and DWU headroom.

Integration boundaries differ. Synapse Dedicated SQL Pool integrates natively with Azure Data Factory, PolyBase/COPY into ADLS Gen2, and Purview for governance, with Azure AD auth as the identity backbone. DWS integrates with DataArts Studio for orchestration and governance, CDM (Cloud Data Migration) and DIS for ingestion, and reads/writes OBS for lakehouse patterns; identity is Huawei Cloud IAM rather than Azure AD, and lineage/catalog tooling is DataArts Architect, not Purview. Expect to refactor ingestion, security, and catalog dependencies rather than re-point existing pipelines.

Start with an assessment inventory: catalog tables, distribution keys, indexed views, stored procedures, external tables, workload classifications, and Azure-ecosystem dependencies (Data Factory, ADLS Gen2, Purview, Azure AD). DWS is the core target for the relational warehouse, but DLI should be evaluated for serverless lake query patterns and MRS for Spark/Hadoop-style workloads that Synapse Spark pools previously served. Do not assume a one-click or schema-level migration path exists; plan a manual T-SQL to PostgreSQL-compatible dialect conversion and validate object-by-object parity.

Move schemas and data with a staged approach: convert DDL and stored procedures to DWS's PostgreSQL-compatible syntax (replacing CREATE TABLE AS SELECT distribution, IDENTITY columns, and T-SQL control flow with DWS equivalents), choose DWS distribution columns to mirror Synapse hash distribution for join-heavy tables, and use CDM to bulk-transfer data from Azure storage or on-prem staging into DWS. For incremental loads, re-architect Azure Data Factory pipelines into DataArts Factory jobs pulling from ADLS/OBS, and use DIS for streaming ingestion where Synapse formerly relied on streaming pipelines.

Validate query semantics, performance, and governance before cutover. Re-benchmark representative workloads against DWS resource pools and Workload Management rather than Synapse DWU envelopes, confirm result parity on partitioned and columnstore tables (DWS uses column-store tables but the storage and index model differs from Synapse columnstore), and migrate catalog/lineage entries into DataArts Architect instead of Purview. Re-map Azure AD-authenticated roles to Huawei IAM and DWS database roles, and verify RBAC and row-level security equivalents, since Synapse row-level security and dynamic data masking do not have identical DWS feature sets.

Account for cost-model and operational gaps. Synapse bills DWU compute plus storage with the option to pause; DWS bills cluster-node flavors plus storage and snapshots while running, so recompute TCO using peak concurrency, retention, backup, and cross-region/interconnect traffic. Regional availability and quotas for large DWS node counts should be confirmed in the target region before sizing, and observability (alerts, slow-query diagnosis) moves from Azure Monitor/Log Analytics to DWS monitoring plus Cloud Eye, which is not a feature-for-feature equivalent.