Single service comparison

Scope and operating model differ sharply. Glue is a general-purpose serverless ETL platform with Spark transforms, Python/Scala jobs, crawlers, a Data Catalog, and orchestration; CDM is a migration-oriented, connector-based data movement service using a distributed framework over roughly twenty source types. CDM offers field mapping, dirty-data archiving to OBS, and full/incremental transfers via WHERE clauses, but it is not an arbitrary Spark transform engine or a managed catalog, so transform-heavy Glue workloads do not map one-to-one.

Scaling, pricing, and operational responsibility diverge. Glue scales serverlessly per job and bills by DPU-hours plus catalog request counts, shifting tuning to capacity and partitioning. CDM runs on purchased cluster instances billed by specification and duration, so you size capacity and pay for reserved runtime regardless of peak utilization. CDM exposes RESTful APIs and a wizard console for links, jobs, and monitoring, but no equivalent of Glue crawlers, Studio, or triggers; operators retain cluster and scheduling responsibilities.

Integrations and governance boundaries differ. Glue ties into the AWS Lake Formation, Athena, Redshift, and S3 data plane with catalog-driven governance. CDM integrates with Huawei MRS (Hive, HBase, HDFS), DWS, RDS, CloudTable, DIS, Elasticsearch, DDS, DCS, OBS, and on-premises stores, but catalog and lineage governance come from complementary services rather than CDM itself. For schema discovery and analytics, expect to pair CDM with DLI or DWS rather than relying on CDM alone.

Start with assessment and target selection. Inventory Glue jobs, crawlers, catalog objects, triggers, and source/target connectors; classify each by transform depth. For movement-heavy, low-transform jobs, map to CDM; for Spark-style transforms and ad hoc analytics, consider DLI for serverless SQL/Spark or DWS for warehouse workloads. Validate connector parity against CDM's supported sources before assuming drop-in replacement, since complex Glue PySpark/Scala logic has no direct CDM equivalent.

Rebuild pipelines in CDM. Define links to source and target systems using CDM's connector framework, configure jobs with field mapping, WHERE-clause or time-variable incremental filters, and dirty-data routing to OBS. Replace Glue crawlers with explicit schema definition or migrations to DLI/DWS catalogs, and replace Glue triggers/workflows with CDM scheduling or an external orchestrator. Re-express transform logic in SQL where possible, since CDM is not an arbitrary code-execution engine.

Validate and cut over with rollback. Run parallel loads and reconcile row counts, checksums, and key business metrics against AWS outputs. Confirm CDM cluster sizing handles peak transfer windows and that incremental jobs deliver required freshness. Establish rollback criteria by retaining source Glue jobs and catalog state until parity is proven across a full cycle, including scheduler behavior, error handling, and dirty-data rates.

Address gaps and recalculate cost. Expect governance, lineage, and catalog feature gaps versus Glue, and fill them with DLI, DWS, or MRS depending on workload. Reshape the cost model from Glue's per-job DPU-runtime and catalog-request billing to CDM's instance-specification-and-duration billing plus network and target-store costs, modeling peak load, retained volume, and cross-region or interconnect traffic before production cutover.