Single service comparison

Dataproc favors short-lived, job-oriented clusters decoupled from GCS, with per-second compute billing plus Dataproc Serverless for Spark batches. Huawei MRS models durable, full-stack Hadoop/Spark/HBase/Flink clusters with cross-AZ HA, heterogeneous Kunpeng/x86 nodes, and Superior Scheduler scaling to tens of thousands of nodes per cluster. MRS emphasizes persistent, enterprise clusters rather than disposable, ephemeral provisioning, so the operating mindset differs even when engines overlap.

Both expose Apache-native APIs (spark-submit, Hive, Presto/Trino, Kafka), but Dataproc ships close-to-upstream images with GCE node autotuning, while MRS blends Huawei-enhanced engine kernels with open-source compatibility. Dataproc integrates natively with BigQuery, Vertex AI, and Cloud Composer; MRS pairs with DataArts Studio for governance, DLI for serverless SQL/Spark, OBS for lake storage, and CloudTable for serving, so metastore, orchestration, and AI tooling dependencies must be remapped rather than assumed portable.

Dataproc treats clusters as disposable with image versioning and component gateway; day-two patching and upgrades are largely Google-managed, while the tenant owns job code and GCS layout. MRS exposes more control-plane surface via Manager UI--rolling patches, Kerberos, cross-cluster trust, and cross-AZ deployment--placing more operational responsibility on the tenant for cluster lifecycle, scaling policies, and component configuration that Dataproc mostly abstracts away.

Inventory Dataproc jobs by engine (Spark, Hive, Flink, HBase), image versions, autoscaling profiles, init actions, and external dependencies on BigQuery, Dataproc Metastore, Pub/Sub, and Cloud Composer. Map persisted Spark/Hadoop workloads to MRS; map ad-hoc serverless SQL/Spark and ETL orchestration to DLI plus DataArts Studio. Treat MRS as the core equivalent capability and reserve DLI/OBS for ingestion, serverless analytics, and governance gaps; do not assume one-to-one feature parity without validation.

Rehome datasets from GCS to OBS using CDM or DistCp over VPC peering, then rebuild Hive metastore and Ranger governance mappings inside MRS. Convert Dataproc init actions to MRS bootstrap scripts, re-create autoscaling policies as MRS scaling rules, and re-point job submission to MRS Manager APIs or Beeline/Livy endpoints. Re-baseline Kerberos, Ranger, and IAM roles into Huawei IAM plus MRS security groups, because authentication and authorization models are not directly portable.

Re-run Spark/Hadoop jars against the equivalent MRS component versions, accounting for Huawei-enhanced kernel differences and validating SQL dialect, UDFs, connectors, and shuffle behavior. Execute parallel dual-run jobs against mirrored OBS data and compare row counts, performance, and lineage with DataArts Studio. Capture failures from image-version skew and tune Spark/Hive parameters against Superior Scheduler and OBS latency before declaring behavioral equivalence.

Schedule cutover only after equivalence tests pass on representative peak and off-peak loads, then shift orchestration from Airflow or Workflows to DataArts Studio. Mind gaps Dataproc does not match: native BigQuery/Vertex AI coupling, Dataproc Serverless Spark, and Cloud Composer--MRS has no identical one-click equivalent and may need DLI plus ModelArts substitutes. Recompute TCO for cluster-spec MRS billing plus OBS storage and inter-region transfer, because per-second Dataproc dosing does not translate directly.