Single service comparison

Service boundary and operating model differ sharply. QuickSight is a fully serverless multi-tenant SaaS where AWS manages compute, the SPICE in-memory cache, capacity, patching, and HA, charging per session/user and SPICE storage. DataArts Insight is Huawei's BI service for connecting to data sources on DWS, DLI, MRS, OBS, and others, but it expects the underlying analytics engines and data storage to be provisioned separately; treat it as the visualization/semantic layer, not a parity-for-parity replacement for SPICE-managed in-memory acceleration.

APIs, semantics, and integrations are not equivalent. QuickSight exposes QuickSight APIs, Embedding APIs, Q topic APIs, and a Template/Dashboard versioning model with row-level security tied to IAM and QuickSight namespaces. DataArts Insight uses its own dataset, dashboard, and permission model integrated with Huawei IAM and dayu/data governance; there is no documented import path for QuickSight analysis/template JSON, so dashboards, calculated fields, RLS rules, and embedded sessions must be rebuilt. Verify supported connectors and SDK support per region, as connector and embedding parity is not guaranteed.

Scaling, HA, and operational responsibility shift to the consumer for the data plane. QuickSight readers scale transparently and SPICE replication is managed. With Huawei, capacity and availability largely follow the chosen engine (DWS cluster spec, DLI compute quotas, MRS nodes) plus DataArts Insight workspace quotas; you own engine tuning, failover, and observability across services. Migration therefore expands operational scope beyond the BI layer into DWS/DLI/MRS management.

Assess QuickSight usage by source: list datasets, SPICE imports, direct-query connections, calculated fields, RLS policies, scheduled refreshes, embedded dashboards, and Q topics. Map each to a Huawei target where DataArts Insight is the visualization/semantic core and DWS or DLI is the query engine depending on whether workloads are warehouse SQL or serverless lake analytics; confirm connector and region support before committing, since not every QuickSight source has a direct equivalent.

Rebuild, do not port, the BI layer. Export QuickSight dataset schemas, SQL, and RLS rules and recreate them as DataArts Insight datasets against DWS/DLI/MRS/OBS; redefine calculated fields, parameters, and dashboard layouts manually because no QuickSight template import is documented. Re-establish scheduled refreshes using DataArts Factory jobs or CDM for ingestion where SPICE refreshes were used, and rework embedded analytics against DataArts Insight embedding/permission APIs with Huawei IAM.

Validate query semantics, performance, and governance before cutover. Reconcile row counts, metric results, RLS behavior, and dashboard rendering against QuickSight originals under representative concurrency; load-test the DWS/DLI engine at peak reader session counts since BI responsiveness now depends on the provisioned engine rather than SPICE. Confirm refresh windows and monitor DLI/DWS cost drivers (compute hours, cluster CU) that QuickSight's per-session model did not expose.

Expect a cost-model change, not a like-for-like bill. QuickSight charges per reader session, author capacity, and SPICE storage; Huawei charges DataArts Insight plus the underlying DWS/DLI/MRS/CDM runtime, storage, and inter-region traffic. Recompute TCO using peak concurrency, query volume, data retention, and refresh frequency, and add governance/observability overhead. Where QuickSight Q anomaly detection or ML insights are used, no direct DataArts Insight equivalent is documented; budget for custom logic or a separate Huawei service and flag this as a functional gap.