Single service comparison

BigQuery is serverless and auto-provisioning: you submit SQL and Google scales compute transparently, billing by bytes processed or reserved slots. Huawei DWS is a managed cluster of GaussDB(DWS) nodes you size and tune, with cluster-spec billing, so capacity planning and node HA are partly your responsibility. DWS offers stronger TP/HTAP behavior, Oracle/Teradata syntax compatibility, and stored procedures, while BigQuery favours decoupled analytical throughput with minimal operational ownership.

For lakehouse and serverless SQL-on-lake needs, Huawei DLI is the closer analog to BigQuery's decoupled model: it runs Spark, Flink, and Trino over OBS data with pay-per-use elastic resource pools and no cluster to manage. DLI targets cross-source analytics on open formats (Parquet, ORC, Hudi) and stream-batch unification, whereas BigQuery tightly couples warehouse storage, BI Engine, and BigLake under one product boundary. Huawei splits these capabilities across DLI (lake) and DWS (warehouse), so no single service mirrors BigQuery's scope.

Integration and operational responsibility differ. BigQuery integrates natively with Dataflow, Dataproc, Looker, and IAM/VPC-SC, with Google handling patching and high availability. Huawei relies on DataArts Studio for orchestration/governance, CDM for batch movement, DIS/DMS for ingestion, and IAM/RBAC, while DWS/DLI HA, backups, and elastic pool sizing are shared responsibilities. Reserve decisions must treat DWS cluster sizing, DLI queue CU limits, and regional service availability as constraints that BigQuery abstracts away.

Assessment and target choice first. Inventory BigQuery datasets, partitioning/clustering strategies, scheduled queries, BI Engine, reservations, ML, and downstream consumers. Use DWS only for governed warehouse workloads needing strong consistency, complex SQL, Oracle-style semantics, or high-concurrency analytics on curated data; use DLI for serverless SQL-on-OBS, stream-batch lakehouse, cross-source federation, and Spark/Flink jobs. Do not treat the two as interchangeable; weight match toward DLI for BigQuery-on-demand elasticity and toward DWS for governed warehouse parity, then add CDM, DIS, and DataArts Studio explicitly.

Data and metadata migration. Use CDM to bulk-move BigQuery tables exported to GCS into OBS or DWS via JDBC, and DIS/DMS for streaming sinks. Recreate datasets as DLI databases or DWS schemas, remap partition/clustering keys to DWS distribution columns or DLI partition specs, and convert BigQuery SQL dialect (e.g., ARRAY/STRUCT, APPROX quantiles, scripting, BI Engine) to DWS GaussDB SQL or DLI Spark SQL/Trino syntax. Replatform scripted and scheduled queries onto DataArts Studio; expect behavioral differences for approximate aggregation, time travel, and materialized views.

Application and tooling cutover. Rewire BI and downstream apps from the BigQuery REST API and client libraries to DWS/DLI JDBC/ODBC endpoints; adjust auth from GCP service accounts to Huawei IAM and network from GCP VPC to Huawei VPC/endpoint gateways. Validate row counts, query result deltas, and SLA latency on representative workloads, and run parallel dry runs. Reserve ML and geospatial workloads for separate evaluation, as DLI/DWS do not provide one-to-one BQML or BigQuery GIS equivalents.

Validation, cost model, and gaps. Recalculate TCO: BigQuery on-demand is bytes-processed plus storage; DWS bills cluster/node-hour plus storage and snapshot costs, while DLI bills CU-hour and scan size per engine. Plan for peak load, reservation sizing, retention, cross-region OBS transfer, and interconnect traffic. Confirm regional availability and quotas for DWS node counts and DLI elastic CUs, and document residual gaps (serverless elastic auto-scale, slot reservation, native time travel, BQML) before production cutover.