Single service comparison

AWS Lake Formation is a metadata and governance control plane layered over S3, exposing the Glue Data Catalog and LF-tag/permission APIs that Athena, EMR, Redshift, and Glue consume. Huawei's DataArts Lake Formation covers the analogous lake setup and catalog/governance role, but its API surface, privilege model (RBAC + policy tags differ from LF-tags), and integration set are Huawei-native and do not provide API-level parity with AWS Lake Formation calls; integration points are DataArts Studio, DLI, DWS, MRS, and OBS rather than Athena/Redshift/EMR. Treat the two as conceptual equivalents, not wire-compatible.

DataArts Fabric is a different workload tier: a serverless, Data+AI lakehouse with a distributed SQL engine and managed Ray for compute, auto-scaling in seconds, and CPU+NPU scheduling. It overlaps Lake Formation only at the analytics lakehouse edge; Fabric does not expose Lake Formation's fine-grained catalog policy constructs or LF-tag inheritance. Use Fabric for elastic query/ML runtime, DataArts Lake Formation for the catalog/governance layer, and combine them rather than substituting one for the other.

Operational responsibility differs: AWS Lake Formation is a regional managed control plane where you own S3 data and IAM/LF policy lifecycle, with HA handled by AWS. Huawei splits this across DataArts Lake Formation (catalog/policy), DataArts Studio (governance/lineage), and OBS (storage), so cross-service IAM, project scoping, and regional availability must be orchestrated by you. Validate regional rollout, quotas, and multi-AZ behavior per service before assuming the same HA and scaling envelope as AWS Lake Formation.

Assess the source lake landscape first: catalog the S3-backed tables, LF-tag policies, column/row filters, IAM roles, and downstream engines (Athena, Redshift, EMR) that consume Lake Formation. Then map the control plane to DataArts Lake Formation (catalog, governance, fine-grained policy) and the analytics/ML runtime to DataArts Fabric or DLI, not as a drop-in replacement. Recreate equivalent RBAC policy tags and project scopes; expect to redesign LF-tag taxonomy and external-engine integration rather than lift it.

For data and metadata migration, move S3 objects to OBS (via CDM/DRS or OBS tools), then rebuild the catalog in DataArts Lake Formation and re-register OBS locations. Reproduce access controls as Huawei RBAC/policy tags and validate column/row filtering semantics per table. There is no automated Lake Formation-to-Huawei migration tool; plan a scripted, table-by-table catalog and policy re-creation with rollback checkpoints, and reconcile S3 path-based permissions into OBS bucket/policy equivalents.

For application migration, re-point query and ETL workloads from Athena/Redshift/EMR to DataArts Fabric SQL, DLI, DWS, or MRS based on workload fit, and rewrite SQL, connectors, and job orchestration for Huawei engines. Use DataArts Studio for lineage, scheduling, and governance to partially restore the Lake Formation control plane. Re-benchmark query plans, concurrency limits, and resource queues, because engine semantics and quotas differ and not all Lake Formation catalog features have direct equivalents.

Validate parity and cutover carefully: compare row counts, schemas, access decisions, and query results; confirm fine-grained policy enforcement, lineage, and audit logging meet compliance needs. Recompute TCO against Huawei's billing—Fabric/ DLI pay-per-use, DWS/MRS cluster-spec, plus OBS storage and transfer—versus AWS Lake Formation's catalog + scanned-data + compute charges. Account for peak load, retention, and cross-region traffic, and accept that governance feature gaps may require manual controls before production cutover.