Single service comparison

Both are managed cluster lifecycle services rather than serverless engines, but the component model differs. HDInsight exposes fixed cluster types (Hadoop, Spark, Kafka, Interactive Query, HBase, ML Services, Storm) each with a constrained bundled version set, terminating cluster types that are being retired. MRS lets you compose a single cluster from many open-source components (Hadoop, Spark, Hive, HBase, Kafka, Flink, Presto, Hudi, Hetu) and ships Huawei-hardened kernels. Treat MRS as the closest equivalent, but do not assume version-for-version or API-for-API parity; remap each workload component to the matching MRS component and validate Hive/Spark/Kafka client compatibility on actual released versions.

Storage and integration boundaries differ. HDInsight is built around decoupled storage on Azure Data Lake Storage Gen2 and Azure Storage, with Microsoft Entra ID auth, Synapse linkage, and is being converged into Azure Fabric; HDInsight on AKS is deprecated. MRS uses OBS-backed object storage or local HDFS, integrates with DataArts Studio for orchestration and governance, DLI for serverless Spark/SQL, and DWS for warehouses. Identity and governance are tied to Huawei IAM and DataArts Studio rather than Entra, Purview, or Synapse, so security, lineage, and metadata flows must be rebuilt rather than ported.

Scaling, HA, and operational responsibility diverge. HDInsight scales by adding worker nodes within region quotas and offers head-node HA per cluster type, with Microsoft handling underlying patching of the managed nodes. MRS supports cross-AZ single-cluster HA, rolling patch/upgrade without interruption, heterogeneous Kunpeng and x86 nodes, and a federation that can scale to very large clusters, while you retain responsibility for component configuration, capacity planning, and cluster lifecycle. Expect to re-baseline HA topology, scaling runbooks, and node spec-to-cost mapping; do not infer identical SLAs or quota ceilings from one platform to the other.

Start with a workload inventory and dependency assessment: list HDInsight cluster types, component versions, libs, Hive/HBASE/Presto SQL dialect usage, Kafka topics, and external schedulers such as Oozie or Airflow. Choose the MRS target per workload variant: MRS for long-running Spark/Hive/Kafka/HBase/Hudi clusters, DLI for serverless Spark/SQL when you want to drop cluster operation, and OBS as the object-store substrate. Do not assume a one-click path exists; HDInsight to MRS is not a documented single-button provider-to-provider migration, so plan a composed, workload-by-workload move.

Migrate data and metadata deliberately. Use CDM for bulk transfer of storage-backed data from Azure blobs or ADLS Gen2 to OBS, and re-create Hive metastore tables against OBS-backed locations on MRS; re-export/import Kerberos, Ranger, and Entaf-linked object ACLs manually into MRS IAM and Ranger equivalents. Re-deploy notebooks, JARs, and pipeline jobs against MRS client endpoints, and convert Oozie/Azure Data Factory orchestration to DataArts Studio workflows, since Azure-side scheduler and ML Services artifacts are not portable as-is.

Validate semantics and performance before cutover. Run query and stream-processing parity checks on representative datasets, comparing row counts, schema drift, Kafka ordering, and Spark job outputs; benchmark latency and throughput against baseline HDInsight runs and confirm MRS cluster spec, OBS throughput class, and region placement meet SLOs. Re-baseline HA with cross-AZ clusters where needed, and rehearse rollback by keeping HDInsight clusters read-only until cutover sign-off.

Account for gaps and cost-model changes. HDInsight on AKS and ML Services have no direct MRS counterpart; replatform those onto DLI, ModelArts, or a custom Spark+ML build rather than assuming parity. Recompute TCO: HDInsight bills per cluster node-hour and per-AKS-managed units plus storage and egress, while MRS bills by node-spec cluster instance, OBS storage and request tiers, and inter-region or cross-AZ transfer; model peak load, retention, and cross-region traffic before production migration, and adjust reserved-instance commitments accordingly.