Single service comparison

Looker's defining asset is its Git-backed LookML semantic layer: metrics and joins are authored as code, versioned, and reused across dashboards, embedded apps, and AI agents, exposing a query and Conversational Analytics REST API. Huawei's closest visual BI service is DataArts Insight, a dashboarding/exploration component inside DataArts Studio that relies on a metadata/physical-model approach in DataArts Architecture rather than a proprietary DSL; it has no documented LookML import or API-for-API parity with Looker's embedding SDKs.

Service boundaries and ecosystem integration differ: Looker runs as a standalone product with tight BigQuery, IAM, and Workspace coupling plus Looker Marketplace blocks, while DataArts Insight is bundled within DataArts Studio packages and is intended to operate over DLI, DWS, MRS, and OBS through the DataArts Catalog governance layer. Organizations migrating cannot lift LookML directly; they must re-express the semantic model in DataArts Architecture and re-wire data source connectors and schedule dependencies on DataArts Factory.

Scaling, embedding, and commercial model diverge substantially. Looker scales horizontally per instance with API-call quotas (e.g., 100k/500k query calls monthly by edition) and licenses by developer/standard/viewer users, while DataArts Studio is sold as combo packages (Integration & Development, Lightweight Governance, Enterprise-Class) with DLI/DWS/MRS runtime or cluster-spec billing. Looker's deep embedding and conversational-agentic capabilities have no confirmed Huawei equivalent; verify regional availability and quota before assuming feature parity for either control or data plane.

Begin with an assessment that inventories LookML models, Explores, dashboards, embedded artifacts, schedules, permissions, and API consumers, classifying each by semantic complexity and embedding depth. Map dashboards and visual exploration to DataArts Insight; reland semantic logic into DataArts Architecture's modeling layer; move data movement into DataArts Migration/CDM and orchestration into DataArts Factory, with DWS or DLI as analytical storage. Document gaps where Looker Marketplace blocks, Looker APIs, or Gemini conversational features have no Huawei equivalent and decide rebuild vs. retire.

For data and configuration migration, first stand up the target warehouse (DWS for governed warehouse semantics, DLI for lake/stream analytics) and use DataArts Migration to load historical BigQuery datasets. Recreate dimensional logic and metric definitions inside DataArts Architecture, rebuild dashboards in DataArts Insight, and reimplement row-level security and folder permissions with DataArts Security and IAM policies. Expect to hand-convert LookML rather than import it, and budget re-platforming effort for any custom data apps that depended on Looker's embedding SDK or query APIs.

Validation and cutover require row-level reconciliation between Looker query results and DataArts Insight dashboards, plus regression of schedules and alerts ported to DataArts Factory. Run parallel reporting, validate performance against your peak concurrency and refresh SLAs, and confirm DataArts Insight availability and quotas in each target region before switching consumers. Re-point embedded use cases to new APIs or interim BI tooling because no automated Looker-to-Huawei cutover path exists.

Recalculate total cost of ownership under Huawei's profile: DataArts Studio package billing plus DWS/DLI/MRS compute, storage, and cross-region traffic, against Looker's platform-plus-user-plus-API-call model. Account for re-platforming labor, lost Marketplace blocks, and any conversational/embedding features you must build or forego. Treat parity as unverified for agentic and embedded analytics; engage Huawei Cloud support to confirm regional quotas, supported data-source versions, and severable components before production.